Methods and systems for automated saturation band placement

ABSTRACT

Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.

TECHNICAL FIELD

Embodiments of the subject matter disclosed herein relate to magneticresonance imaging (MRI). In particular, the current disclosure providessystems and methods for placement of at least one saturation band on alocalizer image based on anatomy present in the localizer image.

BACKGROUND

Magnetic resonance imaging (MRI) is a medical imaging modality that cancreate images of the inside of a human body without using x-rays orother ionizing radiation. MRI systems include a superconducting magnetto create a strong, uniform, static magnetic field Bo. When a humanbody, or part of a human body, is placed in the magnetic field Bo, thenuclear spins associated with the hydrogen nuclei in tissue water becomepolarized, wherein the magnetic moments associated with these spinsbecome preferentially aligned along the direction of the magnetic fieldBo, resulting in a small net tissue magnetization along that axis. MRIsystems also include gradient coils that produce smaller amplitude,spatially-varying magnetic fields with orthogonal axes to spatiallyencode the magnetic resonance (MR) signal by creating a signatureresonance frequency at each location in the body. The hydrogen nucleiare excited by a radio frequency signal at or near the resonancefrequency of the hydrogen nuclei, which add energy to the nuclear spinsystem. As the nuclear spins relax back to their rest energy state, theyrelease the absorbed energy in the form of an RF signal. This RF signal(or MR signal) is detected by one or more RF coils and is transformedinto the image using reconstruction algorithms.

Saturation bands may be used in MRI to suppress an RF signal (or MRsignal) from tissues outside of an imaging region of interest (e.g., ananatomy of interest). Prior to imaging, a saturation band may beprescribed to a localizer image and direct an imaging method or protocolto apply a saturation pulse to the region outlined by the saturationband when scanning for a diagnostic medical image. The saturation pulsemay apply RF energy to suppress the MR signal from moving tissuesoutside of the imaged volume or to reduce and/or eliminate motionartifacts.

SUMMARY

The inventors herein have developed systems and methods which may enableautomatic placement of at least one saturation band on a localizer imageusing a deep neural network, thereby enabling consistency and accuracyin saturation band placement. The current disclosure provides a methodfor acquiring a localizer image of an imaging subject, entering thelocalizer image as input to a deep neural network trained to output aplane mask based on the localizer image, generating a saturation bandbased on the plane mask, and outputting a graphical prescription fordisplay on a display device, the graphical prescription including thesaturation band overlaid on the localizer image. The plane mask may be a3D projection which segments the localizer image as a binary plane mask,such that projecting the plane mask onto the localizer image provideslines on individual slides of localizer data, indicating a 3D plane ofinterest. In this way, anatomical information may be extracted from a 2Dor 3D localizer image by leveraging the deep neural network, such as aconvolutional neural network (CNN), to produce a plane mask for ananatomy of interest. The plane mask may then be used to determine aposition and an orientation (e.g., an angulation) of a saturation band,which may be used along with user input to generate at least onesaturation band. Generation and placement of the at least one saturationband on the localizer image using the CNN may facilitate patientevaluation and diagnosis while reducing a duration of saturation bandplacement prior to scanning.

The above advantages and other advantages, and features of the presentdescription will be readily apparent from the following DetailedDescription when taken alone or in connection with the accompanyingdrawings. It should be understood that the summary above is provided tointroduce in simplified form a selection of concepts that are furtherdescribed in the detailed description. It is not meant to identify keyor essential features of the claimed subject matter, the scope of whichis defined uniquely by the claims that follow the detailed description.Furthermore, the claimed subject matter is not limited toimplementations that solve any disadvantages noted above or in any partof this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of this disclosure may be better understood upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is a workflow of a method for automatically prescribing at leastone saturation band on a localizer image, according to an exemplaryembodiment;

FIG. 2A is a MRI apparatus, according to an exemplary embodiment of thedisclosure;

FIG. 2B is a block diagram of an image processing device which may beincluded in the MRI apparatus, according to an exemplary embodiment;

FIG. 3 is a flow chart illustrating a method for generating andprescribing at least one saturation band on a localizer image based on aplane mask, according to an exemplary embodiment;

FIG. 4 shows a plurality of medical images, each having at least onesaturation band overlaid thereon, according to an exemplary embodiment;

FIG. 5 is a workflow of a method for generating training data which maybe used to train a deep neural network to output a plane mask based on alocalizer image, according to an exemplary embodiment;

FIG. 6 is a flow chart illustrating a method for generating trainingdata, according to an exemplary embodiment;

FIG. 7 shows a plurality of images used to generate training data basedon curvature data, according to an exemplary embodiment;

FIG. 8 shows a plurality of images of a first anatomy of interest usedto generate training data based on bounding boxes, according to anexemplary embodiment;

FIG. 9 shows a plurality of images of a second anatomy of interest usedto generate training data based on bounding boxes, according to anexemplary embodiment; and

FIG. 10 is a flow chart illustrating a method for training a deep neuralnetwork to map at least one plane mask onto a localizer image, accordingto an exemplary embodiment.

Together with the following description, the drawings demonstrate andexplain the structures, methods, and principles described herein. In thedrawings, the size of components may be exaggerated or otherwisemodified for clarity. Well-known structures, materials, or operationsare not shown or described in detail to avoid obscuring aspects of thedescribed components, systems and methods.

DETAILED DESCRIPTION

The following description relates to automatic placement of at least onesaturation band on a localizer image, based on at least one plane maskgenerated using a deep neural network. The disclosure includes aspectsdirected to generating training data for the deep neural network,training said deep neural network, as well as implementing the deepneural network to map the plane mask to the localizer image.

Saturation bands may be used in MRI to suppress an RF signal (or MRsignal) from tissues outside of an imaging region of interest (e.g., ananatomy of interest). Prior to imaging, a saturation band may beprescribed to a localizer image and direct an imaging method or protocolto apply a saturation pulse to the region outlined by the saturationband when scanning for a diagnostic medical image. In some embodiments,the localizer image may be a low-resolution image which may include thesame anatomy of interest as the diagnostic medical image but has a lowerresolution, which may allow for less initial computing demand on an MRIapparatus. When scanning for the diagnostic medical image, thesaturation pulse may apply RF energy to suppress the MR signal frommoving tissues outside of the imaged volume or to reduce and/oreliminate motion artifacts. For example, for a localizer image where theanatomy of interest includes a spine, a saturation band may beprescribed on the localizer image to suppress chest wall and cardiacmotion from “leaking” or otherwise overlapping signals into a spineregion during subsequent acquisition of high resolution data (e.g., thediagnostic medical image). For an anatomy of interest including a lumbarspine region, two saturation bands may be prescribed on the respectivelocalizer image: a first saturation band for a lumbar spine curvature(e.g., a first curvature) and a second saturation band for a sacralspine curvature (e.g., a second curvature). The first saturation bandand the second saturation band may be positioned at differentorientations (e.g., angles) which correspond to the respectivecurvature. When an anatomy of interest is a shoulder, an obliquesaturation band may be prescribed over a chest region to reducepotential breathing artifacts during diagnostic medical image scanning.For time of flight angiography (TOF) imaging, a superior saturation bandmay be applied to the localizer image to suppress potential venoussignal contamination. In some embodiments of pelvic region imaging, atailored saturation band may be placed, where the tailored saturationband is placed along a posterior margin along a midline of a urinarybladder, and the tailored saturation band has a field-of-view length byone third of a maximum anteroposterior length of the pelvis. In magneticresonance spectroscopy imaging (MRSI) of a brain, multiple localizerimages may be prescribed over the patient's head to suppress lipidsignals, thus multiple saturation bands may be prescribed.

Conventionally, a user such as an MRI technologist may manuallyprescribe at least one saturation band on a localizer image. For thescans described above (e.g., lumbar spine, shoulder, TOF, and so on),the MRI technologist may spend considerable time and effort determiningregions of interest and prescribing at least one saturation band tosuppress signals from outside the anatomy of interest. Herein describedare systems and methods for automatic placement of at least onesaturation band on a localizer image based on at least one plane maskgenerated using a deep neural network. The plane mask may be a 3Dprojection which segments the localizer image as a binary plane mask,such that projecting the plane mask onto the localizer image provideslines on individual slides of localizer data, indicating a 3D plane ofinterest. Generation of the at least one saturation band based on acorresponding plane mask of the at least one plane mask may account forpatient position in 3D and allow consistent saturation band placementirrespective of patient position changes. For example, a position and anangulation of a plane mask which are determined to be sufficientparameters for saturation band placement may still allow for sufficientsuppression of signals when used to position the saturation band incircumstances where an imaging subject has changes positions. This mayallow for consistent imaging data to be generated over multiple scanslongitudinally. The disclosure includes aspects directed to generatingtraining data for the deep neural network, training said deep neuralnetwork, as well as implementing the deep neural network to map theplane mask to the localizer image. Automatic placement of the at leastone saturation band using the methods described herein may reduce a timeused to prescribe saturation bands (e.g., compared to manualprescription by a user), which may reduce an overall imaging duration,and may further enabling consistency and accuracy in saturation bandplacement.

FIG. 1 illustrates a workflow for implementing a trained deep neuralnetwork to output at least one plane mask based on an input localizerimage and to further automatically prescribe a corresponding number ofsaturation bands based on the at least one plane mask. FIG. 3 describesa method for automatically prescribing at least one saturation band andperforming a MRI scan based on a graphical prescription, which includesthe at least one saturation band. Examples of graphical prescriptionsincluding at least one saturation band overlaid on a localizer image,which may be generated as described with respect to FIGS. 1, 3 , areshown in FIG. 4 . The deep neural network implemented as described withrespect to FIGS. 1 and 3 may be trained using a plurality of trainingdata pairs generated according to the methods described with respect toFIGS. 5-9 , which may include generating training data based oncurvature data, based on bounding boxes, and/or based on a trainedregression model. The deep neural network may be trained using generatedtraining data as described with respect to FIG. 10 . The workflows andmethods described herein may be implemented by an MRI apparatus, asshown in FIG. 2A, and an image processing device, as shown in FIG. 2B,which may be included in the MRI apparatus.

Turning to FIG. 1 , an exemplary embodiment of a saturation bandprediction workflow 100 is shown. Saturation band prediction workflow100 is configured to acquire a localizer image of an imaging subject andidentify at least one plane mask for the localizer image using a traineddeep neural network. The plane mask may be a 3D projection whichsegments the localizer image as a binary plane mask, such thatprojecting the plane mask onto the localizer image provides lines onindividual slides of localizer data, indicating a 3D plane of interest.Further, saturation band prediction workflow 100 may use a plane fittingmethod to generate a saturation band based on each of the at least oneplane masks. At least one saturation band may be overlaid on thelocalizer image to give a graphical prescription, which may be displayedon a display device. Saturation band prediction workflow 100 may beimplemented by an image processing system of an imaging device, such asa data processing unit 231 (of FIG. 2B) of a magnetic resonance imaging(MRI) apparatus 210 shown in FIG. 2A.

In the embodiment shown in FIG. 1 , and as further described withrespect to FIGS. 4, 7-9 , the region of interest may be an anatomy(e.g., an anatomy of interest), such as an upper spine region, amid-spine region, or a lower spine region. Although the saturation bandprediction workflow 100 is herein described with respect to spineanatomy, the saturation band prediction workflow 100 may be applied toany anatomy or other imaging subject of interest for which suppressionof a signal outside of the anatomy of interest using a saturation bandis desired. Workflows and methods of FIGS. 3, 5-10 may be applied tolocalizer images and/or diagnostic medical images including anatomies orother imaging subjects of interest for which suppression of a signaloutside of the anatomy of interest using a saturation band is desired,as further described herein.

Saturation band prediction workflow 100 may include a deep neuralnetwork configured to receive a localizer image 102 and segment thelocalizer image 102 to generate a plane mask based on the localizerimage 102. The saturation band prediction workflow 100 may receive thelocalizer image 102 from a data acquisition unit 224 (of FIG. 2B) of theMRI apparatus 210 of FIG. 2A. The localizer image 102 may be a localizerimage having a low resolution compared to a diagnostic medical imagecaptured based on the graphical prescription, as further describedherein with respect to FIG. 3 . The localizer image 102 may be a 2D or a3D localizer image and may be captured by the MRI apparatus 210 of FIG.2A during a preliminary imaging scan. For example, the preliminaryimaging scan may include performing an MRI scan without implementingsaturation pulses. In some embodiments, the localizer image 102comprises a matrix of intensity values in one or more color channels(e.g., a pixel-map), wherein each intensity value of each of the one ormore color channels uniquely corresponds to an intensity value for anassociated pixel. The localizer image 102 may include an image of ananatomical region of an imaging subject. In the example shown by FIG. 1, the localizer image 102 is an MRI image of a lower spine region of apatient.

The deep neural network may be a trained convolutional neural network(CNN) 104 comprised of one or more convolutional layers, wherein each ofthe one or more convolutional layers includes one or more filters,comprising a plurality of learnable weights, with a pre-determinedreceptive field and stride. For example, the deep neural network maycomprise a plurality of convolutional filters, wherein a sensitivity ofeach of the plurality of convolutional filters is modulated by acorresponding spatial regularization factor. The trained CNN 104 isconfigured to map features of the localizer image 102 to a plane maskfor at least a first anatomy of interest. Briefly, a localizer image(e.g., the localizer image 102) may be entered as input into the trainedCNN 104, which may then output at least one plane mask based on thelocalizer image 102. In some embodiments, the trained CNN 104 mayidentify an anatomy of interest to which at least one plane mask may bemapped. In other embodiments, a desired anatomy of interest may beselected by a user, such as an MRI technologist, and the selecteddesired anatomy of interest may be input into the trained CNN 104.Details regarding training of the trained CNN 104 are described withrespect to FIG. 10 .

The at least one plane mask may be a binary mask which is generated bysegmenting the localizer image 102 using the trained CNN 104, such thata plane identified by the plane mask is considered as a 3D projection(e.g., lines) on the localizer image 102. For example, a plane mask maybe visualized as a line (e.g., a first plane mask 106, as describedherein with respect to FIG. 1 ), where the line is a projection of aplane from a 3D coordinate system onto the coordinate system of thelocalizer image (e.g., the localizer image 102). Where the segmentedplane is perpendicular to an image plane (e.g., the x-y plane, withrespect to a reference axis system 130), the segmented plane may bevisualized as a line (e.g., the first plane mask 106). The plane maskmay have a variable width, as the segmented plane and the image planemay not perpendicularly intersect. Alternatively, the plane mask mayindicate a line of perpendicular intersection between the segmentedplane and the image plane. In some of a plurality of embodiments, theplane mask comprises a plurality or matrix of values, corresponding tothe plurality of pixel intensity values of the localizer image 102,wherein each value of the plane mask indicates a classification of acorresponding pixel of the localizer image 102. In some embodiments, theplane mask may be a binary segmentation mask, comprising a matrix of 1'sand 0's, wherein a 1 indicates a pixel belongs to an object class ofinterest, and a 0 indicates a pixel does not belong to the object classof interest. In some embodiments, the plane mask may comprise amulti-class segmentation mask, comprising a matrix of N distinctintegers (e.g., 0, 1 . . . N), wherein each distinct integer correspondsuniquely to an object class, thus enabling the multi-class segmentationmask to encode position and area information for a plurality of objectclasses of interest. The values of the plane mask spatially correspondto the pixels and/or intensity values of the localizer image 102, suchthat if the plane mask was overlaid onto the localizer image 102, eachvalue of the plane mask would align with (that is, be overlaid upon) acorresponding pixel of the localizer image 102, which an objectclassification for each pixel of the localizer image would be indicatedby the corresponding value of the plane mask.

As shown in the saturation band prediction workflow 100, the localizerimage 102 may be input into the trained CNN 104. The trained CNN 104 mayidentify at least one plane mask based on the localizer image 102. Forexample, as shown in FIG. 1 , the trained CNN 104 may identify a firstplane mask 106 and a second plane mask 108 for the localizer image 102,which includes a sacral spine curvature and a lumbar spine curvature inthe spine anatomy of interest. As further described with respect toFIGS. 4, 7-9 , for some anatomies, it may be desirable to identify asingle plane mask which may be used to generate a single saturationband. For other anatomies, as described with respect to FIG. 1 , such asa lower spine region, it may be desirable to identify more than oneplane mask which may be used to generate a corresponding number ofsaturation bands. In this way, a signal from anatomies outside theanatomy of interest for regions having curved anatomies may besufficiently suppressed by more than one saturation band, where each ofthe saturation bands are generated based on a respective plane mask.

In some of a plurality of embodiments, the at least one plane maskoverlaid on the localizer image 102 may be displayed on a displaydevice, such as a display device of the MRI apparatus 210 of FIG. 2A.Alternatively, the at least one plane mask overlaid on the localizerimage 102 may not be displayed, and the first plane mask 106 and thesecond plane mask 108 overlaid on the localizer image 102 is shown inFIG. 1 for illustrative purposes.

A saturation band used to suppress signals from a region outside of theanatomy of interest may be generated based on the plane mask identifiedby the trained CNN 104. As described with respect to plane masks, whenmore than one plane mask is generated, a corresponding number ofsaturation bands may be generated, wherein a saturation band isgenerated based on each plane mask. For example, as shown in FIG. 1 , afirst saturation band 116 and a second saturation band 118 are generatedbased on the first plane mask 106 and the second plane mask 108. In theembodiment of FIG. 1 , the first saturation band 116 and the secondsaturation band 118 are represented as boxes. This may be interpreted asareas covered by each of the first saturation band 116 and the secondsaturation band 118 are regions adjacent to the anatomy of interest andregions where a saturation pulse may be applied during a diagnosticscan. Further detail regarding saturation pulses and saturation bandplacement are described with respect to FIGS. 3-4 .

The saturation band may be generated based on the respective plane maskusing a plane fitting method 110. The plane mask identifies a 3D planeof the localizer image and the saturation band is a 2D band (e.g.,single plane) overlaid on the localizer image, therefore the planefitting method 110 may include identifying a position and an angulationof the plane mask and positioning the saturation band on the localizerimage at the position and the angulation of the plane mask. In someembodiments, the plane fitting method 110 is performed as linearregression, using the following equation to fit the plane mask (e.g.,where the plane mask may include a cloud of points in 3D space):

ax+by +cz+d=0

Parameters of the equation represent the normal vector and distance toorigin for the given fitted plane (e.g., the saturation band being fitto the plane mask). Parameters of the equation may be adjusted such thatthe plane of interest passes as close as possible to as many segmentedpoints of interest (e.g., the origin) as possible. This may minimize ametric, such as a sum of squared errors, to align the plane adjacent tothe anatomy of interest.

Using the trained CNN 104 to identify the at least one plane mask andparameters (e.g., position and angulation) of the at least one planemask, then generating at least one saturation band based on the at leastone plane mask may allow for consistency in saturation band placementirrespective of changes in a position of the imaging subject. Forexample, when the imaging subject is a patient, the patient may shiftpositions, change poses, or otherwise move during imaging datacollection (e.g., between the preliminary imaging scan and the imagingscan in which saturation pulses may be used). By identifying at leastone 3D plane using the at least one plane mask and using the at leastone 3D plane to generate at least one saturation band, placement of theat least one saturation band may be consistent with placement of theplane mask and thus consistent imaging data may be generated overmultiple scans.

A width of each of the at least one saturation bands may be determinedin response to input received from a user input device (e.g., theoperating console unit 232). Alternatively, the width may be apre-determined value stored in non-transitory memory as part of animaging protocol, which may direct MR pulses over the imaging region andsaturation pulses over regions indicated by the saturation band, asfurther described herein. The width, the position, and the angulationderived from the plane mask may be used to generate at least onesaturation band, which may be overlaid on the localizer image 102 togive a graphical prescription 112. As further described with respect toFIG. 3 , a method for saturation band prediction may further includeadjustment of a band position and/or a band angulation of the at leastone saturation band by the user, such as a MRI technologist. Followinggeneration of the graphical prescription 112 and optional adjustment ofthe saturation band by the user, a diagnostic scan may be performed bythe MRI apparatus 210 of FIG. 2A according to the graphical prescription112, wherein the diagnostic scan may include performing one or moresaturation pulses at a location dictated by the at least one saturationband. In this way, at least one saturation band may be automaticallypositioned on the localizer image 102 to direct a diagnostic scan, suchthat placement of saturation bands relative to the anatomy of interestis consistent (e.g., with respect to placement of the respective planemask) and the diagnostic scan generates consistent imaging datairrespective of patient pose changes.

Referring to FIG. 2A, an MRI apparatus 210 is shown, in accordance withan exemplary embodiment. The MRI apparatus 210 may be the imaging deviceon which the saturation band prediction workflow 100 is implemented.Further methods and workflows described herein with respect to FIGS.3-10 may also be implemented by the MRI apparatus 210, as furtherdescribed with respect to FIG. 2B.

The MRI apparatus 210 includes a magnetostatic field magnet unit 212, agradient coil unit 213, a RF coil unit 214, a RF body coil unit 215, atransmit/receive (T/R) switch 220, an RF driver unit 222, a gradientcoil driver unit 223, a data acquisition unit 224, a controller unit225, a patient table 226, a data processing unit 231, an operatingconsole unit 232, and a display unit 233. In some embodiments, the RFcoil unit 214 is a surface coil, which is a local coil typically placedproximate to the anatomy of interest of a subject 216. Herein, the RFbody coil unit 215 is a transmit coil that transmits RF signals, and theRF coil unit 214 receives the MR signals. As such, the transmit bodycoil (e.g., RF body coil unit 215) and the surface receive coil (e.g.,RF coil unit 214) are separate but electromagnetically coupledcomponents. The MRI apparatus 210 transmits electromagnetic pulsesignals to the subject 216 placed in an imaging space 218 with a staticmagnetic field formed to perform a scan for obtaining magnetic resonancesignals from the subject 216. One or more images of the subject 216 canbe reconstructed based on the magnetic resonance signals thus obtainedby the scan. The magnetostatic field magnet unit 212 includes, forexample, an annular superconducting magnet, which is mounted within atoroidal vacuum vessel. The magnet defines a cylindrical spacesurrounding the subject 216 and generates a constant primarymagnetostatic field Bo.

The MRI apparatus 210 also includes a gradient coil unit 213 that formsa gradient magnetic field in the imaging space 218 so as to provide themagnetic resonance signals received by the RF coil arrays withthree-dimensional positional information. The gradient coil unit 213includes three gradient coil systems, each of which generates a gradientmagnetic field along one of three spatial axes perpendicular to eachother, and generates a gradient field in each of a frequency encodingdirection, a phase encoding direction, and a slice selection directionin accordance with the imaging condition. More specifically, thegradient coil unit 213 applies a gradient field in the slice selectiondirection (or scan direction) of the subject 216, to select the slice;and the RF body coil unit 215 or the local RF coil arrays may transmitan RF pulse to a selected slice of the subject 216. The gradient coilunit 213 also applies a gradient field in the phase encoding directionof the subject 216 to phase encode the magnetic resonance signals fromthe slice excited by the RF pulse. The gradient coil unit 213 thenapplies a gradient field in the frequency encoding direction of thesubject 216 to frequency encode the magnetic resonance signals from theslice excited by the RF pulse.

The RF coil unit 214 is disposed, for example, to enclose the region tobe imaged of the subject 216. In some examples, the RF coil unit 214 maybe referred to as the surface coil or the receive coil. In the staticmagnetic field space or imaging space 218 where a static magnetic fieldBo is formed by the magnetostatic field magnet unit 212, the RF bodycoil unit 215 transmits, based on a control signal from the controllerunit 225, an RF pulse that is an electromagnet wave to the subject 216and thereby generates a high-frequency magnetic field Bl. This excites aspin of protons in the slice to be imaged of the subject 216. The RFcoil unit 214 receives, as a magnetic resonance signal, theelectromagnetic wave generated when the proton spin thus excited in theslice to be imaged of the subject 216 returns into alignment with theinitial magnetization vector. In some embodiments, the RF coil unit 214may transmit the RF pulse and receive the MR signal. In otherembodiments, the RF coil unit 214 may only be used for receiving the MRsignals, but not transmitting the RF pulse.

The RF body coil unit 215 is disposed, for example, to enclose theimaging space 218, and produces RF magnetic field pulses orthogonal tothe main magnetic field Bo produced by the magnetostatic field magnetunit 212 within the imaging space 218 to excite the nuclei. In contrastto the RF coil unit 214, which may be disconnected from the MRIapparatus 210 and replaced with another RF coil unit, the RF body coilunit 215 is fixedly attached and connected to the MRI apparatus 210.Furthermore, whereas local coils such as the RF coil unit 214 cantransmit to or receive signals from only a localized region of thesubject 216, the RF body coil unit 215 generally has a larger coveragearea. The RF body coil unit 215 may be used to transmit or receivesignals to the whole body of the subject 216, for example. Usingreceive-only local coils and transmit body coils provides a uniform RFexcitation and good image uniformity at the expense of high RF powerdeposited in the subject. For a transmit-receive local coil, the localcoil provides the RF excitation to the anatomy of interest and receivesthe MR signal, thereby decreasing the RF power deposited in the subject.It should be appreciated that the particular use of the RF coil unit 214and/or the RF body coil unit 215 depends on the imaging application.

The T/R switch 220 can selectively electrically connect the RF body coilunit 215 to the data acquisition unit 224 when operating in receivemode, and to the RF driver unit 222 when operating in transmit mode.Similarly, the T/R switch 220 can selectively electrically connect theRF coil unit 214 to the data acquisition unit 224 when the RF coil unit214 operates in receive mode, and to the RF driver unit 222 whenoperating in transmit mode. When the RF coil unit 214 and the RF bodycoil unit 215 are both used in a single scan, for example if the RF coilunit 214 is configured to receive MR signals and the RF body coil unit215 is configured to transmit RF signals, then the T/R switch 220 maydirect control signals from the RF driver unit 222 to the RF body coilunit 215 while directing received MR signals from the RF coil unit 214to the data acquisition unit 224. The coils of the RF body coil unit 215may be configured to operate in a transmit-only mode or atransmit-receive mode. The coils of the RF coil unit 214 may beconfigured to operate in a transmit-receive mode or a receive-only mode.

The RF driver unit 222 includes a gate modulator (not shown), an RFpower amplifier (not shown), and an RF oscillator (not shown) that areused to drive the RF coils (e.g., RF body coil unit 215) and form ahigh-frequency magnetic field in the imaging space 218. The RF driverunit 222 modulates, based on a control signal from the controller unit225 and using the gate modulator, the RF signal received from the RFoscillator into a signal of predetermined timing having a predeterminedenvelope. The RF signal modulated by the gate modulator is amplified bythe RF power amplifier and then output to the RF body coil unit 215.

The gradient coil driver unit 223 drives the gradient coil unit 213based on a control signal from the controller unit 225 and therebygenerates a gradient magnetic field in the imaging space 218. Thegradient coil driver unit 223 includes three systems of driver circuits(not shown) corresponding to the three gradient coil systems included inthe gradient coil unit 213.

The data acquisition unit 224 includes a pre-amplifier (not shown), aphase detector (not shown), and an analog/digital converter (not shown)used to acquire the magnetic resonance signals received by the RF coilunit 214. In the data acquisition unit 224, the phase detector phasedetects, using the output from the RF oscillator of the RF driver unit222 as a reference signal, the magnetic resonance signals received fromthe RF coil unit 214 and amplified by the pre-amplifier, and outputs thephase-detected analog magnetic resonance signals to the analog/digitalconverter for conversion into digital signals. The digital signals thusobtained are output to the data processing unit 231.

The MRI apparatus 210 includes a table 226 for placing the subject 216thereon. The subject 216 may be moved inside and outside the imagingspace 218 by moving the table 226 based on control signals from thecontroller unit 225.

The controller unit 225 includes a computer and a recording medium onwhich a program to be executed by the computer is recorded. The programwhen executed by the computer causes various parts of the apparatus tocarry out operations corresponding to pre-determined scanning. Therecording medium may comprise, for example, a ROM, flexible disk, harddisk, optical disk, magneto-optical disk, CD-ROM, or non-volatile memorycard. The controller unit 225 is connected to the operating console unit232 and processes the operation signals input to the operating consoleunit 232 and furthermore controls the table 226, RF driver unit 222,gradient coil driver unit 223, and data acquisition unit 224 byoutputting control signals to them. The controller unit 225 alsocontrols, to obtain a desired image, the data processing unit 231 andthe display unit 233 based on operation signals received from theoperating console unit 232.

The operating console unit 232 includes user input devices such as atouchscreen, keyboard and a mouse. The operating console unit 232 isused by an operator, for example, to input such data as an imagingprotocol and to set a region where an imaging sequence is to beexecuted. The data about the imaging protocol and the imaging sequenceexecution region are output to the controller unit 225.

The display unit 233 includes a display device and displays an image onthe display screen of the display device based on control signalsreceived from the controller unit 225. The display unit 233 displays,for example, an image regarding an input item about which the operatorinputs operation data from the operating console unit 232. The displayunit 233 also displays a two-dimensional (2D) slice image orthree-dimensional (3D) image of the subject 216 generated by the dataprocessing unit 231. For example, the graphical prescription 112 of FIG.1 may be displayed on the display unit 233, with the at least onesaturation band overlaid on the localizer image 102. Additionally, adiagnostic medical image generated by a diagnostic MRI scan based on thegraphical prescription 112 may be displayed on the display unit 233, asfurther described with respect to FIG. 3 .

The data processing unit 231 includes a computer and a recording mediumon which a program to be executed by the computer to performpredetermined data processing is recorded. The data processing unit 231is connected to the controller unit 225 and performs data processingbased on control signals received from the controller unit 225. The dataprocessing unit 231 is also connected to the data acquisition unit 224and generates spectrum data by applying various image processingoperations to the magnetic resonance signals output from the dataacquisition unit 224.

Turning to FIG. 2B, an image processing device 202 is shown, which maybe implemented as or as an element of the data processing unit 231 ofthe MRI apparatus 210 of FIG. 2A. In some embodiments, at least aportion of the image processing device 202 is disposed at a remotedevice (e.g., edge device, server, etc.) communicably coupled to the MRIapparatus 210 via wired and/or wireless connections. In someembodiments, at least a portion of the image processing device 202 isdisposed at a separate device (e.g., a workstation) which can receiveimages from the MRI apparatus 210 or from a storage device which storesthe images generated by one or more additional imaging systems (e.g.,MRI apparatuses). The image processing device 202 includes a processor204 and a non-transitory memory 206, and is communicatively coupled tothe operating console unit 232, the controller unit 225, and the displayunit 233 of the MRI apparatus 210 of FIG. 2A.

The processor 204 is configured to execute machine readable instructionsstored in non-transitory memory 206. Processor 204 may be single core ormulti-core, and the programs executed thereon may be configured forparallel or distributed processing. In some embodiments, the processor204 may optionally include individual components that are distributedthroughout two or more devices, which may be remotely located and/orconfigured for coordinated processing. In some embodiments, one or moreaspects of the processor 204 may be virtualized and executed byremotely-accessible networked computing devices configured in a cloudcomputing configuration.

Non-transitory memory 206 may store deep neural network module 208,training module 209, and image data 211. For example, each of the deepneural network module 208 and the training module 209 may include codestored in the non-transitory memory 206 which may be executed by theprocessor 204 to implement the deep neural network and generate trainingdata and/or train an untrained deep neural network, respectively. Thedeep neural network (e.g., code of the deep neural network module 208)may be implemented at the data processing unit 231 of the MRI apparatus210. Generation of training data and/or training of an untrained deepneural network may be implemented at the data processing unit 231 of theMRI apparatus 210, on a remote server or computer coupled to the MRIapparatus 210, and so on.

Deep neural network module 208 may include one or more deep neuralnetworks, comprising a plurality of weights and biases, activationfunctions, and instructions for implementing the one or more deep neuralnetworks to receive localizer images and map the localizer images to asegmentation mask. For example, deep neural network module 208 may storeinstructions for implementing a CNN, such as the CNN of the saturationband prediction workflow 100. Deep neural network module 208 may includetrained and/or untrained neural networks and may further include variousmetadata for the one or more trained or untrained deep neural networksstored therein. For example, the deep neural network module 208 mayinclude a trained CNN, such as the trained CNN 104 of FIG. 1 , and/or anuntrained CNN, as further described with respect to FIGS. 5, 10 .

Non-transitory memory 206 may further include training module 209, whichcomprises instructions for training one or more of the deep neuralnetworks stored in deep neural network module 208. Training module 209may include instructions that, when executed by processor 204, causeimage processing device 202 to conduct one or more of the steps ofmethod 1000, discussed in more detail below with reference to FIG. 10 .In one example, training module 209 includes instructions for receivingtraining data pairs from image data 211, wherein said training data paircomprises a medical image and corresponding ground truth plane maskand/or plane parameters for use in training one or more of the deepneural networks stored in deep neural network module 208. In anotherexample, training module 209 may include instructions for generatingtraining data by executing one or more of the operations of the trainingdata generation workflow 500 of FIG. method 600 of FIG. 6 , and methodsdescribed with respect to FIGS. 7-9 , discussed in more detail below. Insome embodiments, the training module 209 is not disposed at the MRIapparatus 210 of FIG. 2A, but is located remotely and communicativelycoupled to the MRI apparatus 210.

As used herein, the terms “system,” “unit,” or “module” may include ahardware and/or software system that operates to perform one or morefunctions. For example, a module, unit, or system may include a computerprocessor, controller, or other logic-based device that performsoperations based on instructions stored on a tangible and non-transitorycomputer readable storage medium, such as a computer memory.Alternatively, a module, unit, or system may include a hard-wired devicethat performs operations based on hard-wired logic of the device.Various modules or units shown in the attached figures may represent thehardware that operates based on software or hardwired instructions, thesoftware that directs hardware to perform the operations, or acombination thereof.

“Systems,” “units,” or “modules” may include or represent hardware andassociated instructions (e.g., software stored on a tangible andnon-transitory computer readable storage medium, such as a computer harddrive, ROM, RAM, or the like) that perform one or more operationsdescribed herein. The hardware may include electronic circuits thatinclude and/or are connected to one or more logic-based devices, such asmicroprocessors, processors, controllers, or the like. These devices maybe off-the-shelf devices that are appropriately programmed or instructedto perform operations described herein from the instructions describedabove. Additionally or alternatively, one or more of these devices maybe hard-wired with logic circuits to perform these operations.

Non-transitory memory 206 may further store image data 211. Image data211 may include localizer images, such as 2D or 3D localizer images ofanatomical regions of one or more imaging subjects. In some embodiments,the images stored in image data 211 may have been acquired by the MRIapparatus 210. In some embodiments, the images stored in image data 211may have been acquired by remotely located imaging systems,communicatively coupled to the MRI apparatus 210. Images stored in imagedata 211 may include metadata pertaining to the images stored therein.In some embodiments, metadata for localizer images stored in image data211 may indicate one or more of image acquisition parameters used toacquire an image, a conversion factor for converting pixel/voxel tophysical size (e.g., converting a pixel or voxel to an area, length, orvolume corresponding to an area length or volume represented by saidpixel/voxel), a date of image acquisition, an anatomy of interestincluded in the image, and so on.

In some embodiments, the non-transitory memory 206 may includecomponents disposed at two or more devices, which may be remotelylocated and/or configured for coordinated processing. In someembodiments, one or more aspects of the non-transitory memory 206 mayinclude remotely-accessible networked storage devices configured in acloud computing configuration. It should be understood that the MRIapparatus 210 shown in FIG. 2A and the image processing device 202 shownin FIG. 2B are for illustration, not for limitation. Another appropriateimage processing system and/or MRI apparatus may include more, fewer, ordifferent components.

It will be appreciated that distinct systems may be used during atraining phase and an implementation phase of one or more of the deepneural networks described herein. In some embodiments, a first systemmay be used to train a deep neural network by executing one or moresteps of a training method, such as method 1000 described below, and asecond separate system may be used to implement the deep neural networkto prescribe at least one saturation band for a localizer image, such asby executing one or more of the steps of method 300, described below.Further, in some embodiments, training data generation may be performedby a third system, distinct from the first system and the second system,by executing one or more steps of method 600 and/or methods describedwith respect to FIGS. 5, 7-9 , described below. As such, the firstsystem, the second system, and the third system, may each comprisedistinct components. In some embodiments, the second system may notinclude a training module, such as training module 209, as deep neuralnetworks stored on non-transitory memory of the second system may bepre-trained by the first system. In some embodiments, the first systemmay not include an imaging device, and may receive images acquired byexternal systems communicably coupled thereto. However, in someembodiments a single system may conduct one or more or each of trainingdata generation, deep neural network training, and implementation of thetrained deep neural networks, disclosed herein.

As described above, placement of a saturation band on a localizer image(e.g., a 2D or 3D localizer image) may suppress a signal from outside ofan anatomy of interest by indicating a region to be targeted by asaturation pulse during diagnostic imaging (e.g., to generate adiagnostic medical image). Referring to FIG. 3 , a flow chart of amethod 300 for generating and prescribing at least one saturation bandon a localizer image based on a plane mask is shown. In someembodiments, the method 300 may be implemented by an imaging system,such as the MRI apparatus 210 of FIG. 2A, which may include the imageprocessing device 202 of FIG. 2B.

At operation 302, the imaging system acquires a localizer image of ananatomical region of an imaging subject. The localizer image may be a 2Dor a 3D localizer (e.g., a single or multi-plane) image generated from aMRI scan, and may have a first resolution. The first resolution may be alow resolution, compared to a resolution of a diagnostic medical image.For example, the localizer image may be the localizer image 102 of FIG.1 . In some embodiments, at operation 302 the imaging system acquiresthe localizer image using an imaging device, such as the MRI apparatus210. For example, a preliminary scan may be performed to acquire thelocalizer image, where the preliminary scan may include a differentintensity of MR pulses, compared to a diagnostic scan. In otherembodiments, the imaging system receives the localizer image from anexternal device communicatively coupled to the imaging system, such asan image repository. The localizer image received at operation 302 maycomprise a plurality of intensity values in one or more color channels,corresponding to a plurality of pixels. The plurality of intensityvalues may be arranged in a definite order. In some embodiments, theplurality of intensity values of the localizer image may comprise a 2Dor 3D array or matrix, wherein each intensity value of the plurality ofintensity values in a particular color channel may be uniquelyidentified by a first index and a second index, such as by a row numberand a column number. In embodiments where the localizer image includes aplurality of color channels, the color channel to which an intensityvalue corresponds may be further indicated by a third index. The imagemay comprise a grayscale image or color image.

At operation 304, the imaging system maps a region of interest to aplane mask using a convolutional neural network (CNN). For example, theregion of interest may be an anatomy of interest, such as a spineregion, a shoulder region, a pelvic region, and so on. The method 300 isdescribed herein with respect to FIG. 1 , where the anatomy of interestis a mid-spine region, however the method 300 may also be used to mapother anatomies of interest to a plane mask. The localizer imageacquired at operation 302 may be entered as input into the CNN, which istrained to output at least one plane mask based on the localizer image.A plane mask of the at least one plane mask may be positioned adjacentto the anatomy of interest, such that a saturation band placed at thesame position and angulation as the plane mask may suppress a signalfrom the underlying region (e.g., adjacent to the anatomy of interest)during a diagnostic scan.

For example, a localizer image may include an anatomy of interest withan anterior-most point, a posterior-most point, an inferior-most pointand a superior-most point, where each of an anterior, a posterior, aninferior, and a superior direction are coplanar (e.g., the y-x planewith respect to the reference axis system 130 of FIG. 1 ) and theanterior and posterior directions are coaxial (e.g., the x-axis withrespect to the reference axis system 130) and the inferior and superiordirections are coaxial (e.g., the y-axis with respect to the referenceaxis system 130). The anterior-most point of the anatomy of interest maybe a point at which the anatomy of interest is positioned posterior tothe anterior-most point, and a region on an anterior side of theanterior-most point of the anatomy of interest does not include theanatomy of interest. Positioning a plane mask adjacent to the anatomy ofinterest may include positioning the plane mask at the anterior-mostpoint of the anatomy of interest, where the plane mask is a linear planethat bisects the localizer image, such that a region on an anterior sideof the plane mask does not include the anatomy of interest and a regionon a posterior side of the plane mask includes the anatomy of interest.

For anatomies of interest which include curved regions, such asdescribed with respect to FIG. 1 , a linear plane positioned at theanterior-most point of the anatomy of interest may include anatomiesother than the anatomy of interest in the region on the posterior sideof the linear plane. Thus, an additional linear plane (e.g., plane mask)may be desired to exclude anatomies other than the anatomy of interestfrom the posterior side of the linear plane, such that signals fromanatomies other than the anatomy of interest may be suppressed duringdiagnostic image capture. An anatomy of interest, such as a spine regionincluding a lumbar spine curvature and a sacral spine curvature, asdescribed with respect to FIG. 1 , may have an inferior-most point inaddition to the anterior-most point Similar to the anterior-most point,the inferior-most point of the anatomy of interest may be a point atwhich the anatomy of interest is positioned in a region superior to theinferior-most point. A region on the inferior side of the inferior-mostpoint of the anatomy of interest does not include the anatomy ofinterest. Positioning a plane mask adjacent to the anatomy of interestmay include positioning the plane mask at the inferior-most point of theanatomy of interest, where the plane mask is a linear plane that bisectsthe localizer image, such that a region on a superior side of the planemask includes the anatomy of interest and a region on an inferior sideof the plane mask does not include the anatomy of interest.

Saturation bands may be positioned at each of the planes positioned atthe anterior-most point and the inferior-most point of the anatomy ofinterest. A width of the plane (e.g., along the x-axis, with respect tothe reference axis system 130 of FIG. 1 for the second plane mask 108)may be any width extending in the anterior direction, relative to theanatomy of interest. A posterior side 134 of the second plane mask 108may indicate the plane positioned at the anterior-most point of theanatomy of interest. As described with respect to FIG. 1 and furtherdescribed herein, the second saturation band 118 may be positioned atthe same position and angulation as the second plane mask 108. Forexample, a posterior-most side 144 of the second saturation band 118 maybe positioned at the anterior-most point of the anatomy of interest. Inother words, a position along the x-axis (with respect to the referenceaxis system 130) of the posterior side 134 of the second plane mask 108may be equal to a position along the x-axis of the posterior-most side144 of the second saturation band 118. For the sacral spine curvature, asuperior side 154 of the first plane mask 106 may indicate the planepositioned at the inferior-most point of the anatomy of interest. Thefirst saturation band 116 may be positioned at the same position anangulation as the first plane mask 106. For example, a superior-mostside 164 of the first saturation band 116 may be positioned at theinferior-most point of the anatomy of interest. In other words, aposition along the x- and y-axes (e.g., position and angulation, whereangulation is an angle of the plane with respect to the y-axis) of thesuperior side 154 of the first plane mask 106 may be equal to a positionalong the x- and y-axes of the superior-most side 164 of the firstsaturation band 116. Placement of a saturation band (e.g., the firstsaturation band 116 and the second saturation band 118) may indicate aregion for a saturation pulse during a diagnostic scan (as furtherdescribed with respect to FIG. 3 ). Further examples of position andangulation of saturation bands based on a corresponding plane mask aredescribed with respect to FIG. 4 .

The CNN may be trained identify a number of plane masks, as well as aposition and an angulation of the plane mask, based on anatomy includedin the anatomy of interest. For example, as described above, differentanatomies may have a different number of desired saturation bands aswell as different positioning of the desired saturation bands, based onthe anatomy. As described with respect to FIG. 1 and further describedwith respect to FIGS. 4, 5, 7 , and 8, it may be desirable to identifytwo plane masks for a lower spine region including a sacral spine curveand a lumbar spine curve, thus allowing prescription of twocorresponding saturation bands. In some embodiments, the CNN may betrained to identify anatomies within the anatomy of interest anddetermine a desired number of plane masks based on an anatomy ofinterest.

In other embodiments, a user may select an anatomy of interest to bescanned, where selection of the anatomy of interest includes selectingan anatomy of a plurality of anatomies listed on a user interface. Forexample, the plurality of anatomies may include an upper spine region, amid-spine region, a lower spine region, a shoulder region, a pelvicregion, and so on. The user may further select a time of flightangiography (TOF) scan. Based on the anatomy of interest and/or the scanselected by the user, the CNN may identify the anatomy of interest usingfeature mapping, in some embodiments.

In further embodiments, the user may position an imaging subject suchthat the anatomy of interest is positioned within a scan plane (e.g.,within an area where the MRI apparatus may scan, such as the RF coilunit 214 of FIG. 2A). Upon initiation of the method 300, the MRIapparatus may scan the region present in the scan plane and at least oneplane mask may be generated based on what the CNN is trained to identifyas potential regions of interest.

The plane mask may be generated based on an underlying anatomy presentin the localizer image. The CNN may identify at least one plane adjacentto the anatomy of interest and model the at least one plane as a 3Dprojection (e.g., a line projection, herein referred to as a plane mask)on the localizer image. The CNN may further segment the localizer imageas a binary plane mask. In some embodiments, at least one plane mask maybe overlaid on the localizer image and displayed on a display device.Alternatively, metadata of a position and an angulation of each of theat least one plane masks may be associated with a respective localizerimage and may not be displayed on the display device.

At 306, the method 300 includes generating a saturation band based onthe plane mask. When more than one plane mask is generated (e.g., outputby the CNN based on the input localizer image), a saturation band may begenerated based on each plane mask. Generating the saturation band mayinclude, at operation 308, fitting the saturation band to planeparameters of the plane mask via plane fitting and, at operation 310,generating the saturation band based on an imaging protocol (e.g., basedon a preset or user input width of the saturation band). Plane fittingmay include identifying parameters of the plane mask, including aposition and an angulation of the plane mask. For example, the positionof the plane mask may include positioning along a horizontal axis of thelocalizer image, such as along an x-axis with respect to the referenceaxis system 130 of FIG. 1 . A length of the plane mask may extend alength of the localizer image (e.g., along a y-axis with respect to thereference axis system 130) when the plane mask is vertical (e.g.,parallel to the y-axis). The plane mask may also be positioned at anangle, where angulation of the plane mask may be in a y-x plane. Whenpositioned at an angle, the length of the plane mask may extend a widthof the localizer image (e.g., along the x-axis). The plane mask may havea predetermined width which is not equal to a width of the saturationband. A side of the plane mask (e.g., a superior side 132 of the firstplane mask 106 or a posterior side 134 of the second plane mask 108 ofFIG. 1 ) may be adjacent to the anatomy of interest and thepredetermined width of the plane mask may extend in a direction oppositethe side of the plane mask adjacent to the anatomy of interest. Asdescribed above, the plane mask may be a 3D projection displayed as aline. Therefore, the plane mask may indicate the position and angulationof a plane which extends into the localizer image (e.g., in a directionof a z-axis, with respect to the reference axis system 130), and thepredetermined width of the plane mask may be irrelevant, as the side ofthe plane mask adjacent to the anatomy of interest identifies a plane ofinterest.

Plane parameters (e.g., position and angulation) may be identified bythe CNN and output along with the plane mask as metadata of thelocalizer image, in some embodiments. In other embodiments, the planemask may be output by the CNN as lines overlaid on the localizer image(e.g., as shown in FIG. 1 ) and plane parameters may be determined as anoperation of plane fitting. Plane fitting may further include overlayinga saturation band on the localizer image at the position and angulationof the corresponding plane mask. The saturation band may be a 2Dprojection on the localizer image (e.g., a 2D or 3D localizer image) andmay therefore not extend along the z-axis, counter to the plane mask. Alength and a width of the saturation band may be equal to the length andthe width of the plane mask, in some embodiments. In other embodiments,the length and the width of the saturation band may be predeterminedbased on the anatomy of the anatomy of interest, which may be determinedby the CNN and/or preset by a user, as described above. The saturationband may have a predetermined width which may be adjusted by a user. Forexample, a user may input a desired width for a saturation band based ona selected anatomy to be imaged. Additionally or alternatively, thesaturation band width may be preset (e.g., may be saved as part of animaging protocol) and correspond to a selected anatomy of interest. Eachsaturation band of a plurality of saturation bands generated for asingle localizer image may have the same width or may have differentwidths. In this way, at least one saturation band may be generated for alocalizer image, where a number of saturation bands is equal to a numberof plane masks output by the CNN.

At 312, the method 300 includes outputting a graphical prescription fordisplay on a display device. The graphical prescription may include atleast one saturation band overlaid on the localizer image used to mapthe at least one plane mask, where each saturation band of the at leastone saturation band is generated based on a corresponding number ofplane masks. The display device may be the display device of the displayunit 233 of the MRI apparatus 210 of FIG. 2A. A user, such as a MRItechnologist, may optionally adjust a band position and a bandangulation of at least one saturation band of the graphicalprescription. For example, the user may adjust the band position and/orband angulation of a saturation band which is at least partiallyoverlapping the anatomy of interest so that signal from the anatomy ofinterest may not be suppressed during the diagnostic scan. At 314, thegraphical prescription may be adjusted based on the user input (e.g., toadjust the band position and/or the band angulation). Adjustments to thegraphical prescription based on the user input may be shown on thedisplay device in real time or may be periodically updated to be shownon the display device.

At 316, the method 300 includes performing a diagnostic scan of theimaging subject according to the graphical prescription. The graphicalprescription includes the localizer image which may have a firstresolution which is lower than a desired resolution for analysis anddiagnosis. Performing the diagnostic scan may include performing MRimaging, as described above, to acquire a diagnostic medical image ofthe same anatomy of interest as the localizer image with a higherresolution than the localizer image. Further, the diagnostic medicalimage may include suppressed signals from regions outside of the anatomyof interest (e.g., regions covered by the at least one saturation band).The diagnostic medical image may be acquired by applying conventional MRsignals to the imaging subject in the imaging region (e.g., positionedin the RF coil unit 214 of FIG. 2A), and applying saturation pulses toregions dictated (e.g., outlined) by the at least one saturation band(e.g., as indicated by the graphical prescription). The saturation pulsemay apply RF energy to suppress the MR signal from moving tissuesoutside of the imaged volume or to reduce and/or eliminate motionartifacts.

In some embodiments, acquisition of a localizer image (e.g., operation302 of method 300) may be performed based on user input as describedherein. Briefly, a user may select a desired anatomy of interest to beimaged from a list of anatomies provided on a display unit 233 using auser input device (e.g., the operating console unit 232 of FIG. 2A).Alternatively, an anatomy of interest may be automatically identifiedbased on anatomical landmarks in the imaging region, and a localizerimage may be captured which includes the automatically identifiedanatomy of interest. Mapping the region of interest (e.g., the anatomyof interest) to the plane mask is performed automatically by the CNN,and generation of the saturation band based on the plane mask isperformed automatically by the processor using plane fitting. Thegenerated plane mask is output for display on the display device, anduser input may be used to adjust at least one of a position, anangulation, or a width of the saturation band. The user may provideinput to the control unit via the operating console unit 232 that theposition, angulation, and width of the saturation band are sufficient,and the diagnostic scan may be automatically performed, where theprocessor automatically identifies regions indicated by the at least onesaturation band where a saturation pulse is desired. A diagnostic imagegenerated by the diagnostic scan may be output for display on thedisplay device and/or may be stored in the non-transitory memory 206 ofthe image processing device 202.

Patient motion may occur between capturing of the localizer image (e.g.,the localizer image from which at least one plane mask is generated) andthe diagnostic scan to capture the diagnostic medical image. Still, thesaturation band may sufficiently cover regions outside of the anatomy ofinterest, and therefore suppress signal from tissues outside the anatomyof interest. Because the plane mask is a 3D projection into anatomy ofthe imaging subject, a 2D saturation band having a band position and aband angulation equal to that of the plane mask may allow for consistentimaging data to be generated regardless of patient motion.

Turning to FIG. 4 , a plurality of graphical prescriptions 400 is shown,wherein each graphical prescription of the plurality of graphicalprescriptions 400 includes at least one saturation band overlaid on alocalizer image. The at least one saturation band may be generated basedon at least one plane map output by a CNN trained to output a plane maskbased on a localizer image, such as described with respect to the method300 of FIG. 3 . The plurality of graphical prescriptions 400 may beexamples of graphical prescriptions output for display on a displaydevice, such as the display unit 233 of the MRI apparatus 210 of FIG.2A. Each of the plurality of graphical prescriptions 400 may thus beused to direct a diagnostic scan performed by a MRI apparatus, such asthe MRI apparatus 210 of FIG. 2A.

A first graphical prescription 410 may include a first localizer image412 with a third saturation band 416 overlaid thereon. As described withrespect to the saturation band prediction workflow 100 of FIG. 1 and themethod 300 of FIG. 3 , a plane mask may be generated by the CNN based onan underlying anatomy of the first localizer image 412, and the thirdsaturation band 416 may be generated based on the plane mask. In thefirst localizer image 412, an anatomy of interest includes an upperspine region of a patient. It may be determined by the CNN that, for ananatomy of interest (e.g., the upper spine region) of the firstlocalizer image 412, a single saturation band may sufficiently blocksignals generated by anatomies outside of the anatomy of interest. TheCNN may therefore generate a single plane mask in alignment with ananterior-most point of the anatomy of interest. The third saturationband 416 may be generated based on the plane mask, wherein a bandposition and a band angulation of the third saturation band 416 areequal to a position and an angulation of the plane mask. A width of thethird saturation band 416 may be input by a user, such as an MRItechnologist, as described with respect to method 300.

The first graphical prescription 410 further includes a first boundingbox 414 overlaid on the first localizer image 412, where the firstbounding box 414 indicates an imaging region. In some embodiments, thefirst bounding box 414 may not be input to the CNN when generating theplane mask. Instead, the first bounding box 414 may indicate a region tobe imaged during a diagnostic scan by the MRI apparatus. In otherembodiments, the first bounding box 414 may be placed by a user orautomatically overlaid on the first localizer image 412 based onpositioning of an imaging subject in a scan plane. When the firstlocalizer image 412 including the first bounding box 414 is input intothe CNN, the CNN may prescribe a plane mask based on an anatomy withinthe first bounding box 414.

As shown in the first graphical prescription 410, the third saturationband 416 may extend beyond the first bounding box 414. Inclusion of thefirst bounding box 414 in the first graphical prescription 410 mayfurther assist a technologist in deciding whether to adjust the thirdsaturation band 416, which has been automatically prescribed accordingto the saturation band prediction workflow 100 of FIG. 1 and the method300 of FIG. 3 . For example, the user may adjust at least one of theband position, the band angulation, and the width of the thirdsaturation band 416. Following optional adjustment of the thirdsaturation band 416, a diagnostic scan may be performed according to thefirst graphical prescription 410. In addition to a conventional MRIscanning procedure, the diagnostic scan may include implementing asaturation pulse in the region defined by the third saturation band 416.In this way, signals from tissues in the region of the third saturationband 416 may be blocked or suppressed, which may reduce signalinterference and generate a diagnostic medical image wherein the anatomyof interest may be clearly distinguished from other anatomies. This mayassist in patient diagnostics.

The plurality of graphical prescriptions 400 further includes a secondgraphical prescription 420, which may include a second localizer image422 with a fourth saturation band 426 overlaid thereon. As describedwith respect to the saturation band prediction workflow 100 of FIG. 1and the method 300 of FIG. 3 , a plane mask may be generated by the CNNbased on an underlying anatomy of the second localizer image 422, andthe fourth saturation band 426 may be generated based on the plane mask.In the second localizer image 422, an anatomy of interest includes amid-spine region of a patient. It may be determined by the CNN that, foran anatomy of interest (e.g., the mid-spine region) of the secondlocalizer image 422, a single saturation band may sufficiently blocksignals generated by anatomies outside of the anatomy of interest. TheCNN may therefore generate a single plane mask in alignment with ananterior most point of the anatomy of interest. The fourth saturationband 426 may be generated based on the plane mask, wherein a bandposition and a band angulation of the fourth saturation band 426 areequal to a position and an angulation of the plane mask. A width of thefourth saturation band 426 may be input by the user, as described withrespect to method 300.

The second graphical prescription 420 further includes a second boundingbox 424 overlaid on the second localizer image 422, where the secondbounding box 424 indicates the imaging region. Similar to the firstbounding box 414, the second bounding box 424 may not be input to theCNN when generating the plane mask. Instead, the second bounding box 424may indicate a region to be imaged during a diagnostic scan by the MRIapparatus. In other embodiments, the second bounding box 424 may beplaced by a user or automatically overlaid on the second localizer image422 based on positioning of an imaging subject in a scan plane. When thesecond localizer image 422 including the second bounding box 424 isinput into the CNN, the CNN may prescribe a plane mask based on ananatomy within the second bounding box 424.

As shown in the second graphical prescription 420, the fourth saturationband 426 may extend beyond the second bounding box 424. Inclusion of thesecond bounding box 424 in the second graphical prescription 420 mayfurther assist a technologist in deciding whether to adjust the fourthsaturation band 426, which has been automatically prescribed accordingto the saturation band prediction workflow 100 of FIG. 1 and the method300 of FIG. 3 . For example, the user may adjust at least one of theband position, the band angulation, and the width of the fourthsaturation band 426. Following optional adjustment of the fourthsaturation band 426, a diagnostic scan may be performed according to thesecond graphical prescription 420. In addition to a conventional MRIscanning procedure, the diagnostic scan may include implementing asaturation pulse in the region defined by the fourth saturation band426. In this way, signals from tissues in the region of the fourthsaturation band 426 may be blocked and/or suppressed, which may reducesignal interference and generate a diagnostic medical image wherein theanatomy of interest may be clearly distinguished from other anatomies.This may assist in patient diagnostics.

The plurality of graphical prescriptions 400 further includes a thirdgraphical prescription 430, which may include a third localizer image432 with a fifth saturation band 436 and a sixth saturation band 438overlaid thereon. As described with respect to the saturation bandprediction workflow 100 of FIG. 1 and the method 300 of FIG. 3 , morethan one plane mask may be generated by the CNN based on an underlyinganatomy of the third localizer image 432, where the underlying anatomyhas at least one curvature. For example, an anatomy of interest of thethird localizer image 432 includes a lower spine region, comprising alumbar spine curvature (e.g., a first curvature) and a sacral spinecurvature (e.g., a second curvature). Each of the lumbar spine curvatureand the sacral spine curvature may be approximately aligned along adifferent plane. For example, the lumbar spine curvature may beproximate to a vertical plane and the sacral spine curvature may beproximate to a horizontal plane. It may be determined by the CNN thatmore than one saturation band is desired to sufficiently block signalsfrom anatomies outside of the anatomy of interest. The CNN may thereforegenerate a first plane mask to identify a plane adjacent to an anteriormost point of the lumbar spine curvature and a second plane mask toidentify a plane adjacent to an inferior most point of the sacral spinecurvature. The fifth saturation band 436 and the sixth saturation band438 may be generated based on the first plane mask and the second planemask, respectively. A band position and a band angulation of the fifthsaturation band 436 may be equal to a position and an angulation of thefirst plane mask, and a band position and a band angulation of the sixthsaturation band 438 may be equal to a position and an angulation of thesecond plane mask. A width of each of the fifth saturation band 436 andthe sixth saturation band 438 may be input by the user, and may be equalor different.

Inclusion of the fifth saturation band 436 and not the sixth saturationband 438 may allow signal from regions outside of the sacral spinecurvature (e.g., inferior to the sacral spine curvature) to interferewith signal from both the lumbar spine curvature and signal from thesacral spine curvature. Inclusion of the sixth saturation band 438 andnot the fifth saturation band 436 may allow signal from regions outsideof the lumbar spine curvature (e.g., anterior to the lumbar spinecurvature) to interfere with signal from both the lumbar spine curvatureand signal from the sacral spine curvature. Therefore, it is desirableto include both the fifth saturation band 436 and the sixth saturationband 438, such that signal of the anatomy of interest is not interferedwith by signal from tissues outside the anatomy of interest.

The third graphical prescription 430 further includes a third boundingbox 434 overlaid on the third localizer image 432, where the thirdbounding box 434 indicates the imaging region. Similar to the firstbounding box 414 and the second bounding box 424, the third bounding box434 may not be input to the CNN when generating the first plane mask andthe second plane mask. Instead, the third bounding box 434 may indicatea region to be imaged during a diagnostic scan by the MRI apparatus. Inother embodiments, the third bounding box 434 may be placed by a user orautomatically overlaid on the third localizer image 432 based onpositioning of an imaging subject in a scan plane. When the thirdlocalizer image 432 including the third bounding box 434 is input intothe CNN, the CNN may prescribe a plane mask based on an anatomy withinthe third bounding box 434.

As shown in the third graphical prescription 430, the fifth saturationband 436 and the sixth saturation band 438 may extend beyond the thirdbounding box 434. Inclusion of the third bounding box 434 in the thirdgraphical prescription 430 may further assist a technologist in decidingwhether to adjust either or both of the fifth saturation band 436 andthe sixth saturation band 438, which have been automatically prescribedaccording to the saturation band prediction workflow 100 of FIG. 1 andthe method 300 of FIG. 3 . For example, the user may adjust at least oneof the band position, the band angulation, and the width of either orboth of the fifth saturation band 436 and the sixth saturation band 438.Following optional adjustment of either or both of the fifth saturationband 436 and the sixth saturation band 438, a diagnostic scan may beperformed according to the third graphical prescription 430. In additionto a conventional MRI scanning procedure, the diagnostic scan mayinclude implementing a saturation pulse in the region defined by thefifth saturation band 436 and the sixth saturation band 438. In thisway, signals from tissues in the region of the fifth saturation band 436and the sixth saturation band 438 may be blocked and/or suppressed,which may reduce signal interference and generate a diagnostic medicalimage wherein the anatomy of interest may be clearly distinguished fromother anatomies. This may assist in patient diagnostics.

Described with relation to FIGS. 1-4 are systems, methods, andembodiments thereof for generating at least one saturation band based onat least one plane mask output by a CNN. A localizer image is input intothe CNN, which is trained to output at least one plane mask based on alocalizer image. Plane fitting is performed to position a saturationband at a position and an angulation of each of the at least one planemasks. A graphical prescription, which includes at least one saturationband overlaid on the localizer image, is output for display on a displaydevice and may be used to perform a diagnostic scan. The diagnostic scanmay include at least one saturation pulse at regions outlined by the atleast one saturation band to block signals from tissues outside of theanatomy of interest. By first identifying a plane adjacent to theanatomy of interest, where the plane (e.g., which may be indicated bythe plane mask) is a 3D projection extending into the localizer image,which may be a 2D or 3D image, the corresponding saturation band maysufficiently suppress signals outside of the anatomy of interest,regardless of potential imaging subject movement or pose changes betweena preliminary imaging scan and a diagnostic scan.

Prior to implementation of the CNN, the CNN is trained to map an anatomyof interest to at least one plane mask based on the underlying anatomyof the localizer image. Training data used to train the CNN may beautomatically generated, as is described with respect to a training datageneration workflow of FIG. 5 and a method of generating training dataof FIG. 6 . Training data may be generated in more than one way, such asbased on curvature data, as described with respect to FIG. 7 , and/orbased on bounding boxes, as described with respect to FIGS. 8-9 .Generated training data may be input into an untrained CNN to train theuntrained CNN to output at least one plane mask based on an anatomy of alocalizer image. Training of the untrained CNN is described with respectto FIG. 10 . A resulting trained CNN may be implemented in thesaturation band prediction workflow 100 of FIG. 1 and the method 300 ofFIG. 3 , as described above.

Turning to FIG. 5 , an exemplary embodiment of a training datageneration workflow 500 is shown. Briefly, the training data generationworkflow 500 includes acquiring a medical image of an imaging subject,generating at least one saturation band based on the medical image, andidentifying one or more plane parameters of the at least one saturationband by generating a plane projection based on the saturation band. Thetraining data generation workflow 500 may be implemented by one or moreof the systems disclosed herein, such as the MRI apparatus 210 of FIG.2A and/or the image processing device 202 of FIG. 2B.

Training data generation workflow 500 is configured to generate trainingdata pairs, which may include a medical image, such as a 2D or 3Dlocalizer image captured during a diagnostic scan (e.g., a diagnosticmedical image), and an associated ground truth. The associated groundtruth may include a set of plane parameters (e.g., ground truthparameters) for a plane from which a desired saturation band may begenerated, including a plane position and a plane angulation.Additionally or alternatively, the associated ground truth may be agraphical projection including a plane projection overlaid on thediagnostic medical image. Training data pairs generated by the trainingdata generation workflow 500 may be employed in a training method, suchas a method 1000 of FIG. 10 , to train a deep neural network forgenerating and outputting at least one plane mask based on a localizerimage, such as the trained CNN 104 of the saturation band predictionworkflow 100 of FIG. 1 and the CNN of the method 300 of FIG. 3 .

The training data generation workflow 500 may acquire a medical image,such as a diagnostic medical image 502, generated from a diagnostic MRIscan. The diagnostic medical image 502 may be a 2D or a 3D localizerimage with a high resolution (e.g., compared to a localizer image whichmay be captured during a preliminary scan). In some embodiments, theimaging system acquires the diagnostic medical image 502 using animaging device, such as the MRI apparatus 210. In other embodiments, theimaging system receives the diagnostic medical image 502 from anexternal device communicatively coupled to the imaging system, such asan image repository.

The training data generation workflow 500 may further include inputtingthe diagnostic medical image 502 into a segmentation method which maygenerate a segmentation mask of the diagnostic medical image 502 toidentify a region of interest of the diagnostic medical image 502. Theregion of interest may include an anatomy of interest, such as an upperspine region, a mid-spine region, a shoulder, and so on. In theembodiment shown in FIG. 5 , the diagnostic medical image 502 includesan anatomy of interest including a lower spine region comprised of alumbar spine curvature (e.g., a first curvature) and a sacral spinecurvature (e.g., a second curvature). Anatomical landmarks of theanatomy of interest which may be used for saturation band placement maybe identified using the segmentation mask.

The training data generation workflow 500 further comprises generating afirst graphical prescription 504, wherein the graphical prescriptionincludes the diagnostic medical image 502 with at least one saturationband overlaid thereon. The at least one saturation band is a 2Dprojection on the diagnostic medical image 502, which may be a 2D or a3D localizer image. In some embodiments, the at least one saturationband may be manually placed on the diagnostic medical image 502 by auser, such as an MRI technologist or other user trained to placesaturation bands on diagnostic medical images. In other embodiments, theat least one saturation band may be automatically placed by a traineddeep neural network. For example, the diagnostic medical image may beinput into a deep neural network trained to output a graphicalprescription (e.g., the first graphical prescription 504) including atleast one saturation band overlaid on the diagnostic medical image.

In the embodiment shown in FIG. 5 , a seventh saturation band 516 and aneighth saturation band 518 are overlaid on the diagnostic medical image502. The seventh saturation band 516 is aligned along a length of theseventh saturation band 516 with an anterior most point of the lumbarspine curvature and the eighth saturation band 518 is aligned along alength of the eighth saturation band 518 with an inferior most point ofthe sacral spine curvature, such that a width of each of the at leastone saturation bands extends away from the anatomy of interest. Both theseventh saturation band 516 and the eighth saturation band 518 aretherefore positioned to block signals from tissues other than those inthe lower spine region during a MRI scan.

In the training data generation workflow 500, a MRI scan may not beperformed and instead the first graphical prescription 504, includingthe at least one saturation band, is converted to a second graphicalprescription 506, which includes at least one plane projection toidentify the one or more plane parameters. Conversion of the firstgraphical prescription 504 may include indicating a desired planeprojection at a band position and a band angulation of each of the atleast one saturation bands. The at least one plane projection mayidentify a 3D plane of the diagnostic medical image 502, which may be a2D or a 3D localizer image captured by a diagnostic MRI scan. The secondgraphical prescription 506 thus includes the diagnostic medical image502 with a number of plane projections overlaid thereon, where thenumber of plane projections is equal to a number of saturation bandsoverlaid on the first graphical prescription 504. In the embodiment ofFIG. 5 , a first plane projection 526 is overlaid on the diagnosticmedical image 502 in place of the seventh saturation band 516 and asecond plane projection 528 is overlaid on the diagnostic medical image502 in place of the eighth saturation band 518. Converting each of theat least one saturation band to the at least one plane projection mayinclude identifying one or more plane parameters of the saturation bandand identifying a plane having equal parameters. For example, a bandposition and a band angulation of the eighth saturation band 518 isequal to a position and an angulation of the second plane projection528.

Each of the seventh saturation band 516 and the eighth saturation band518 have an associated width, which may be input by a user or may be apre-set value. As previously stated, each of the at least one saturationband is a 2D projection overlaid on the diagnostic medical image 502,wherein each of the at least one saturation band has a length and awidth. Each of the first plane projection 526 and the second planeprojection 528 may be a 3D projection overlaid on the diagnostic medicalimage 502, wherein each of the at least one plane projection has alength and a width, as well as a depth which extends into a thickness ofthe diagnostic medical image 502 when the diagnostic medical image 502is a 3D image. A position of each of the at least one plane projectionmay be based on a side of the respective saturation band adjacent to theanatomy of interest. For example, the first plane projection 526 may bepositioned such that a posterior side 546 of the first plane projection526 is at a position and an angulation of a posterior side 536 of theseventh saturation band 516.

In some embodiments, the training data pair may include the diagnosticmedical image with at least one plane projection overlaid thereon (e.g.,the second graphical prescription 506), as described with respect toFIG. 5 . In other embodiments, the training data pair may include thediagnostic medical image with associated metadata indicating a positionand an angulation of at least one plane projection identified accordingto the workflow described with respect to FIG. 5 and/or the method 600of FIG. 6 . As further described herein with respect to FIGS. 6-9 ,training data pair may be generated according to at least one of aplurality of methods, where plane parameters for at least one planeprojection for a diagnostic medical image may be determined based oncurvature data, based on at least one bounding box of the diagnosticmedical image, and/or using a trained regression network.

Turning now to FIG. 6 , a method 600 for generating training data (e.g.,a diagnostic medical image and one or more plane parameters therefore)is shown. As described with respect to the training data generationworkflow 500, generating training data may include acquiring adiagnostic medical image of an imaging subject, generating a saturationband based on the diagnostic medical image, and identifying one or moreplane parameters of the saturation band. As described herein withrespect to FIG. 6 , the saturation band may be generated using aplurality of methods, which may include fitting a bounding box toanatomical landmarks and/or using anatomy curvature information. Themethod 600 may be executed by one or more of the systems describedherein, such as the MRI apparatus 210 of FIG. 2A, and/or the imageprocessing device 202 of FIG. 2B. Method 600 may be executed as part ofa workflow for generating training data, such as the training datageneration workflow 500.

At 602, the method 600 includes acquiring a diagnostic medical image.The diagnostic medical image may be a high-resolution image compared toa localizer image which may be input to a CNN trained to output at leastone mask from which a corresponding number of saturation bands may begenerated, in accordance with FIGS. 1 and 3 , and may be captured by adiagnostic MRI scan. In some embodiments, the imaging systemimplementing the method 600 receives the localizer from an externaldevice communicatively coupled to the imaging system, such as an imagerepository. The diagnostic medical image acquired at operation 602 maycomprise a plurality of intensity values in one or more color channels,corresponding to a plurality of pixels. The plurality of intensityvalues may be arranged in a definite order. In some embodiments, theplurality of intensity values of the diagnostic medical image maycomprise a 2D or 3D array or matrix, wherein each intensity value of theplurality of intensity values in a particular color channel may beuniquely identified by a first index and a second index, such as by arow number and a column number. In embodiments where the diagnosticmedical image includes a plurality of color channels, the color channelto which an intensity value corresponds may be further indicated by athird index. The diagnostic medical image may comprise a grayscale imageor color image.

At 604, the method 600 includes identifying anatomical landmarks forsaturation band placement. For example, within a region of interestidentified by a user or within an imaging region captured in the image,the method 600 may identify anatomies of interest and determine acorresponding anatomy. In the examples of FIGS. 5 and 7 , identificationof a sinusoidal curved spine region may indicate that the anatomy ofinterest is a lower spine region for which more than one saturation bandmay be desired. In the example of FIG. 9 , a concave or convex curvedspine region may indicate a mid- or upper spine region, for which asingle saturation band may sufficiently block or suppress signals fromoutside the anatomy of interest. Generation of saturation bands fordifferent anatomies is further described with respect to FIGS. 7-9 .

In some embodiments, identifying anatomical landmarks for saturationband placement may be done by generating a segmentation mask of thediagnostic medical image to identify the anatomy of interest. In otherembodiments, a deep learning neural network may be trained to identifyanatomies of interest in a diagnostic medical image. The trained deeplearning neural network may be implemented in the method 600 to identifyanatomical landmarks. Other methods for identifying anatomical landmarksand, based on identified anatomical landmarks, identifying a number andapproximate orientation of saturation bands, may include manual useridentification and/or other machine learning-based methods.

At 606, the method 600 includes generating a ground-truth plane forsaturation band placement. Identifying anatomical landmarks at theoperation 604 may indicate a number and approximate placement ofsaturation bands for the diagnostic medical image and operation 606 mayinclude positioning (e.g., adjusting approximate placement of) each ofthe at least one saturation bands on the diagnostic medical image. Asdescribed with respect to FIGS. 1 and 5 , a side of a saturation bandmay be positioned adjacent to, but not overlapping, an anatomy ofinterest. For example, as described with respect to FIG. 5 , theposterior side 536 of the seventh saturation band 516 may be adjacent toan anterior side of the lumbar spine curvature. At least one saturationband (e.g., where a number of saturation bands may correspond to ananatomy of interest) may be positioned by a user, using a trained deepneural network, or another method for positioning a saturation bandrelative to an anatomy of interest.

For example, at 608, the method 600 may include generating at least onesaturation band based on curvature data of the anatomy of interest.Turning to FIG. 7 , a plurality of images 700 demonstrating generationof training data based on curvature data are shown. As described withrespect to FIG. 6 , generating training data may include generating asegmentation mask of a diagnostic medical image to identify an anatomyof interest of the diagnostic medical image. In the embodiment shown inFIG. 7 , the method 600 may identify the anatomy of interest as thelower spine region based on anatomical landmarks and infer that theanatomy captured by a diagnostic medical image 750 includes lumbarvertebrae and the sacrum.

A graphical prescription 702 of the diagnostic medical image 750 shows asegmentation mask 704 of a lower spine region, including a firstcurvature (e.g., a lumbar spine curvature) and a second curvature (e.g.,a sacral spine curvature). The lumbar spine curvature may be representedas a first region 706 of the segmentation mask 704 and the sacral spinecurvature may be represented as a second region 708 of the segmentationmask 704. A dashed line runs through a center of the segmentation mask704 and indicates each vertebra of the lower spine region with a point.Together, the points and the dashed line indicate curvature of the lowerspine region. The graphical prescription 702 further includes ananterior saturation band 716 and an inferior saturation band 718.Positioning of each of the anterior saturation band 716 and the inferiorsaturation band 718 may be determined based on curvature-basedthresholds.

For example, the dashed line of the segmentation mask 704 may beapproximately linear above a horizontal dashed line 710. As describedwith respect to operation 604 of FIG. 6 , generating a saturation bandbased on curvature data may include positioning an anterior saturationband adjacent to an approximately linear region of the lower spineregion. Additionally or alternatively, the anterior saturation band maybe positioned based on anatomical landmarks (e.g., as identified atoperation 604). For example, the anterior saturation band may bepositioned parallel to the dashed line of the segmentation mask from aL1, T11, or T12 vertebrae to a L4 or L3 vertebrae, as indicated on thegraphical prescription 702. A first curvature-based threshold may beused to select a superior vertebra to fit the anterior saturation bandto (e.g., from among the L1, T11, or T12 vertebrae). For example, thefirst curvature-based threshold may be a percentage or angle ofdeviation from a linear line between a superior-most vertebra (e.g.,T12) and other vertebrae in the lumbar spine curve (e.g., the T11, L1,L2, L3, L4, and L5 vertebrae). The superior vertebra used to fit theanterior saturation band may be selected as the most superior vertebra(e.g., of the L1, T12, and T11 vertebrae) which is in linear alignmentwith other vertebrae of the lumbar spine curve. For example, if the T12vertebra deviates from linear alignment with other vertebra by at least30 degrees, the T11 vertebra may be selected as the superior vertebra tofit the anterior saturation band. The first curvature-based thresholdmay be set and/or adjusted by a user, determined by a deep neuralnetwork, or any other sufficient methods. In the embodiment shown inFIG. 7 , the anterior saturation band 716 may be positionedapproximately parallel to the dashed line of the segmentation mask 704between the T12 and the L3 vertebrae. In this way, the anteriorsaturation band (e.g., the anterior saturation band 716) is fit toanterior points of the segmentation mask 704 and the anterior saturationband is parallel to a plane defined by the linear portion of the dashedline of the segmentation mask 704. Offset of the anterior saturationband from an anterior-most point of the segmentation mask 704 may bepre-determined, for example, may be offset by a pre-determined distanceinput by a user or based on the anatomy identified in the graphicalprescription 702.

For anatomies in which a second saturation band is desired, such as thelow spine region, an inferior saturation band for the sacral spine curvemay also be positioned based on curvature data. The inferior saturationband may be positioned based on an approximate alignment of an S1vertebra and the rest of the sacrum (e.g., the second region 708).Similar to placement of the anterior saturation band, the inferiorsaturation band may be positioned parallel to a linear region of thedashed line of the segmentation mask 704 in the second region 708. Theinferior saturation band may be offset from an inferior-most point ofthe segmentation mask 704 based on a user defined offset and/or apre-determined distance. In the embodiment shown in FIG. 7 , theinferior saturation band 718 may be approximately parallel to the dashedline of the segmentation mask 704 along the sacrum and L5 vertebra.

In this way, at least one saturation band may be generated based oncurvature data (e.g., operation 606 of FIG. 6 ). A segmentation mask maybe generated based on the diagnostic medical image to indicate ananatomy of interest (e.g., identified by the operation 604 of the method600). At least one saturation band may be position based on anteriorpoints and/or inferior points of the anatomy of interest (e.g., theanterior saturation band and/or the inferior saturation band). A firstcurvature-based threshold may be used to identify a superior region(e.g., superior vertebra) of interest used to position the anteriorsaturation band, and the anterior saturation band may be positionedparallel to anterior points of the superior region. The anteriorsaturation band may be offset from the superior region by apre-determined (e.g., user-defined) distance. The inferior saturationband may be positioned parallel to an inferior region of the anatomy ofinterest based on a second curvature-based threshold and may be offsetfrom the inferior region by a pre-determined (e.g., user-defined)distance, which may be equal to or different from the offset of theanterior saturation band from the superior region.

Additionally or alternatively, at least one saturation band may begenerated based on regions of the anatomy of interest. Returning to FIG.6 , at 610, the method 600 may include generating at least onesaturation band based on at least one bounding box. Briefly, this mayinclude mapping at least one bounding box to an anatomy of interest,identifying a plane having a normal closest to a cosine direction of thesegmentation mask, using cosine directions from the segmentation mask toidentify a first direction of a bounding box which has a highestsimilarity with a second direction (e.g., opposite the first direction),adjusting a center point of the bounding box distal from the normal ineither first direction or the second direction, and identifying planeparameters of the plane. Turning to FIGS. 8 and 9 , a plurality ofimages is shown for generating saturation bands based on bounding boxesfor a lower spine region, an upper spine region, and a mid-spine region.

FIG. 8 shows a plurality of segmentation mask images 800, where asegmentation mask 850 is shown for a lower spine region. A firstsegmentation mask image 802 indicates a lumbar spine curve of a lowspine region, a second segmentation mask 804 indicates a sacral spinecurve of the low spine region, and a third segmentation mask 806 showsboth of the lumbar spine curve and the sacral spine curve as arrangedwith respect to each other in the low spine region. As described withrespect to FIG. 6 , the method 600 may determine that more than onesaturation bands are desired for the anatomy shown in the diagnosticmedical image (e.g., the lower spine region) based on anatomicallandmarks. Generating at least one saturation band may include mappingat least one bounding box to the anatomy of interest, where a number ofbounding boxes is equal to a desired number of saturation bands. In theembodiment shown in FIG. 8 , a first bounding box 812 may be mapped tothe lumbar spine curve of the low spine region and a second bounding box814 may be mapped to the sacral spine curve of the low spine region.Voxels of the segmentation mask 850 may be on one side of a plane of abounding box and thus be tangential to the bounding box. For example,voxels of the segmentation mask 850 which are tangential to therespective bounding box (e.g., of the first bounding box 812 and thesecond bounding box 814) are shown in FIG. 8 as being within the boundsof the respective bounding box. Generating a saturation band based onthe bounding box includes identifying a plane of the bounding box whichhas a normal closest to an anterior-posterior direction of the anatomyof interest. The anterior-posterior direction may be a conventionalanterior-posterior direction used when referring to an imaging subject,where a chest is anterior and a back is posterior. Left, posterior,superior (LPS) cosine directions of the segmentation mask 850 may beused to identify a plane of a bounding box having the normal closest tothe anterior-posterior direction. A direction of the bounding box whichhas a highest cosine similarity with the posterior (P) direction may beidentified as being closes to the anterior-posterior direction and acorresponding plane may be selected for generation of a saturation band.Generating a saturation band may include shifting a selected plane byhalf of a thickness of the respective bounding box in the normaldirection.

In the embodiment shown in FIG. 8 , a first plane 822 of the firstbounding box 812 is determined to have a normal closest to theanterior-posterior direction of planes of the first bounding box 812. Asecond plane 824 of the second bounding box 814 is determined to have anormal closest to the anterior-posterior direction of planes of thesecond bounding box 814. A first saturation band 832 may be positionedsuch that the first saturation band 832 does not overlap with a regiondefined by the first bounding box 812 (e.g., the lumbar spine curve). Asecond saturation band 834 may be positioned such that the secondsaturation band 834 does not overlap with a region defined by the secondbounding box 814.

Saturation bands may also be generated based on bounding boxes foranatomies other than the lower spine region, such as a mid-spine regionand the upper spine region. Turning to FIG. 9 , a plurality ofsegmentation masks 900 of a mid-spine region (e.g., a thoracic spineregion) and an upper spine region (e.g., a cervical spine region) areshown, with corresponding saturation bands positioned adjacently. Afirst segmentation mask image 902 includes a thoracic segmentation mask912 and a thoracic saturation band 922. A second segmentation mask image904 includes a cervical segmentation mask 914 and a cervical saturationband 924. Each of the thoracic segmentation mask 912 and the cervicalsegmentation mask 914 may be generated based on a bounding box (notshown in FIG. 9 ) according to the method described with respect to FIG.8 .

Returning to operation 606, the method 600 may convert each of the atleast one saturation band generated according to the methods describedwith respect to FIGS. 7-9 , or other sufficient methods for generatingsaturation bands, to a ground truth plane. The ground truth plane may beequivalent to the plane projection as described with respect to FIG. 5 .A position and an angulation of the ground truth plane (e.g., groundtruth parameters) may equal a position and a band angulation of thecorresponding saturation band, such that the ground truth plane isoverlaid to align with a side of the saturation band most proximate tothe anatomy of interest. In some embodiments, the ground truth plane maynot be overlaid on the diagnostic medical image and instead the groundtruth parameters (e.g., the position and the angulation of the groundtruth plane) may be included in metadata of the diagnostic medical imageto form a training data pair.

At 612, the method 600 includes outputting the diagnostic medical imageand corresponding ground truth plane(s). The diagnostic medical imageand corresponding ground truth plane(s), including ground truth planeparameters of position and angulation, may be output as a training datapair to an image repository or other training data repository from whichtraining data may be sources to train a CNN to output at least one planemask based on a localizer image input into the CNN. For example,training data pair (e.g., training data) may be stored as image data 211on the non-transitory memory 206 of the image processing device 202 ofFIG. 2B. A plurality of training data pair may be used to train the CNNimplemented in the saturation band prediction workflow 100 and/or themethod 300. Additionally or alternatively to the methods described withrespect to FIGS. 5-9 , training data may be generated using a regressionnetwork, which may be trained to predict ground-truth plane parameters,such as a position and an angulation of a ground-truth plane.

Turning now to FIG. 10 , a flow chart is shown for a method 10000 fortraining a deep neural network (such as a CNN implemented as describedwith respect to FIGS. 1 and 3 ) to output at least one plane mask basedon a localizer image input into the CNN. The CNN may be trained using aplurality of training data pair, which may be generated according to themethods described above, or other sufficient methods for determiningparameters for at least one plane based on a saturation band of alocalizer image. In some embodiments, the method 1000 may be implementedby an imaging system, such as the MRI apparatus 210 of FIG. 2A, whichmay include the image processing device 202 of FIG. 2B. Alternatively,the method 1000 may be implemented at an image processing device notcoupled to an imaging system, such as the MRI apparatus 210. The CNN maybe trained at a first system (e.g., an image processing device 202) andimplemented at a second system (e.g., the MRI apparatus 210).

As described above, a training data pair may include a diagnosticmedical image and plane parameters for at least one plane projectiongenerated based on a corresponding number of saturation bands for thediagnostic medical image (e.g., as described with respect to FIGS. 5-9). A plurality of training data pairs may be used to train a CNN.Briefly, a diagnostic medical image of a training data pair may be inputinto the untrained CNN. The untrained CNN may map a set of predictedplane parameters (e.g., at least one plane mask) based on the diagnosticmedical image. A loss may be determined between the set of predictedplane parameters and plane parameters for the associated planeprojection(s) of the diagnostic medical image. Weights and biases of theuntrained CNN may be adjusted based on the loss. This process may berepeated for a plurality of training data pairs until loss calculated isbelow a desirable error threshold, at which point the CNN may beconsidered to be trained and may be implemented in methods foroutputting at least one plane mask (e.g., and associated planeparameters) based on a localizer image.

Method 1000 begins at operation 1002, where a training data pair, from aplurality of training data pairs, is input into a deep neural network(e.g., a CNN), wherein the training data pair comprises a diagnosticmedical image of an anatomical region of an imaging subject, andcorresponding plane parameters for at least one plane projectionindicating a position of a saturation band for the anatomical region ofthe diagnostic medical image. In some embodiments, the training datapair, and the plurality of training data pairs, may be stored in animaging system, such as in image data 211 of the image processing device202. In other embodiments, the training data pair may be acquired viacommunicative coupling between the imaging system and an externalstorage device, such as via Internet connection to a remote server.

At operation 1004, the imaging system may extract features from thediagnostic medical image using a feature extractor. In some embodiments,the feature extractor comprises one or more learnable/adjustableparameters, and in such embodiments, said parameters may be learned byexecution of method 1000. In some embodiments, the feature extractorcomprises hard-coded parameters, and does not includelearnable/adjustable parameters, and in such embodiments the featureextractor is not trained during execution of method 1000. In otherembodiments, the imaging system may identify anatomies of the diagnosticmedical image by prescribing a segmentation mask to the diagnosticmedical image, as described with respect to FIGS. 5-9 .

At operation 1006, the imaging system maps the features to at least onepredicted plane mask for the anatomy of interest identified at operation1004, using a deep neural network. In some embodiments, the deep neuralnetwork comprises a CNN, comprising one or more convolutional layers,comprising one more convolutional filters. The deep neural network mapsthe features to a predicted plane mask by propagating the features fromthe input layer, through one or more hidden layers, until reaching anoutput layer of the deep neural network. The predicted plane mask mayinclude associated predicted plane parameters, including a position andan angulation of the plane mask. The predicted plane mask may be a 3Dprojection on the diagnostic medical image to identify a plane adjacentto the anatomy of interest.

At operation 1008, the imaging system calculates a loss for thepredicted plane parameters (e.g., of each predicted plane mask of the atleast one plane mask) based on a difference between the predicted planeparameters and the ground truth plane parameters (e.g., the planeparameters of the diagnostic medical image included in the training datapair). In one embodiment, the loss comprises a mean-squared-error, givenby the following equation:

${MSE} = {\frac{1}{N}{\sum\limits_{i = 0}^{N}\left( {x_{i} - X_{i}} \right)^{2}}}$

Where MSE stands for mean-squared-error, N is the total number oftraining data pairs, i is an index indicating the currently selectedtraining data pair, x_(i) is a predicted thickness mask for trainingdata pair i, and X_(i) is a ground truth thickness mask for trainingdata pair i. The expression x_(i)-X_(i) will be understood to representpair-wise subtraction of each pair of corresponding thickness values inthe predicted thickness mask and the ground truth thickness mask, forthe currently selected training data pair i. It will be appreciated thatother loss functions known in the art of machine learning may beemployed at operation 1008.

At operation 1010, the weights and biases of the deep neural network areadjusted based on the loss determined at operation 1008. In someembodiments, the parameters of the feature extractor, and the CNN, maybe adjusted to reduce the loss over a set of training data pair. In someembodiments, the feature extractor may not include a learnableparameter, and therefore operation 1010 may not include adjustingparameters of the feature extractor. In some embodiments, backpropagation of the loss may occur according to a gradient descentalgorithm, wherein a gradient of the loss function (a first derivative,or approximation of the first derivative) is determined for each weightand bias of the deep neural network. Each weight (and bias) of the deepneural network is then updated by adding the negative of the product ofthe gradient determined (or approximated) for the weight (or bias) witha predetermined step size. Method 1000 may then end. It will be notedthat method 1000 may be repeated for each of a plurality of trainingdata pairs in a training data set, and this process may be repeateduntil a stop condition is met. Wherein, in some embodiments, the stopcondition comprises one or more of the loss decreasing to below athreshold loss, a rate of loss change decreasing to below a thresholdrate of loss change, a validation loss, determined over a validationdata set, reaching a minimum, and so on. In this way, a CNN may learn tomap said at least one plane mask to an anatomy of interest for adiagnostic medical image.

As described herein, training data pairs including a diagnostic medicalimage and plane parameters based on desired saturation bands for thediagnostic medical image may be generated, and the generated trainingdata pairs may be used to train a deep neural network, such as a CNN, tooutput at least one plane mask based on a localizer image input into theCNN. Each of the at least one plane mask may be a 3D projectionindicating a plane adjacent to an anatomy of interest. A plane mask mayindicate a band position and a band angulation at which a saturationband may be placed on the diagnostic medical image. A graphicalprescription including at least one saturation band based on acorresponding number of plane masks may be output for display on adisplay device. Following optional adjustment by a user, a diagnosticscan may be performed using a MRI apparatus, in which saturation pulsesare directed to regions indicated by the at least one saturation band.Identifying the at least one saturation band (e.g., a 2D plane overlaidon the diagnostic medical image) based on a corresponding plane mask(e.g., a 3D projection) may allow for consistent imaging data to becaptured over multiple scans longitudinally. The plane mask may identifya 3D plane and therefore allow for consistent saturation band placement,where the saturation band may be positioned based on the plane mask andsufficiently suppress signals in a region covered by the saturationband, irrespective of patient pose. A duration of image capture as wellas a level of user error may decrease, as automated prescription of atleast one saturation band may decrease user input during image capture.

Automatic placement of a saturation band on a localizer image at aposition and an angulation of a plane mask, where the plane mask isgenerated by a deep neural network based on the localizer image, mayallow for more consistency and accuracy in saturation band placement.This may provide consistency in diagnostic images captured based ongraphical prescriptions including at least one saturation band overlaidon the image, where the at least one saturation band indicates a regionwhere it is desired to apply a saturation pulse and therefore suppresssignal from underlying anatomy. Additionally, automatic positioning ofthe saturation band based on the plane mask may improve processingefficiency of diagnostic image generation. For example, training a deepneural network, such as a CNN as described herein, to identify aposition and an angulation of a plane at an anterior-most orinferior-most point of an anatomy of interest may provide a method forgenerating at least one plane mask based on the anatomy of interest formultiple different anatomies using less data and with faster processingthan conventional approaches. Other image processing methods may includeidentifying saturation band position and angulation based on a specificanatomy, for example, positioning a saturation band for an upper spineregion at a first location, where the first location is predeterminedand prescription of the saturation band is contingent on positioning ofthe anatomy of interest (e.g., the upper spine region) in an imagingregion. This may not account for the anatomy of interest as a 3D image,as the saturation band is a 2D projection on the localizer image.Instead, using the position and the angulation of the plane mask toposition the saturation band may allow for the saturation band to bepositioned with respect to the anatomy of interest as a 3D image andpotential differences in the anatomy of interest (e.g., lesions, foreignobjects, and so on) which may provide challenges to placement of thesaturation band based on a predetermined position. The systems andmethods described herein may improve processing efficiency for placementof at least one saturation band. Using a trained deep neural network togenerate a plane mask based on a localizer image and placing asaturation band based on placement of the plane mask provides anefficient method for determining, via more efficient calculations,saturation band position. Processing efficiency of automated placementof at least one saturation band may thus be improved. For example,because the saturation band placement position and/or orientation may beimpacted by complex geometrical considerations, basing the determiningon the mask enables the processing by the processor to be faster andmore efficient. Further, by using the trained network to generate themask of the localizer image, and then determining the saturation basedon the band, a more efficient approach is used where less data isprocessed and less complex algorithms may be implemented on theprocessor in order to generate the saturation band.

The disclosure also provides support for a method, comprising: acquiringa localizer image of an imaging subject, determining a plane mask forthe localizer image by entering the localizer image as input to a deepneural network trained to output the plane mask based on the localizerimage, generating a saturation band based on the plane mask bypositioning the saturation band at a position and an angulation of theplane mask, and outputting a graphical prescription for display on adisplay device, the graphical prescription including the saturation bandoverlaid on the localizer image. In a first example of the method, theplane mask is a projection of a plane of a 3D coordinate system onto thelocalizer image. In a second example of the method, optionally includingthe first example, overlaying the saturation band on the localizer imageincludes positioning the saturation band at the position and theangulation of the plane mask. In a third example of the method,optionally including one or both of the first and second examples, atleast one of a position and an angulation of the saturation band aredetermined based on plane fitting plane parameters of the plane mask. Ina fourth example of the method, optionally including one or more or eachof the first through third examples, the method further comprises:determining a width of the saturation band in response to input receivedfrom a user input device. In a fifth example of the method, optionallyincluding one or more or each of the first through fourth examples, themethod further comprises: adjusting the graphical prescription,including at least one of a position, an angulation, and a width of thesaturation band, based on user input. In a sixth example of the method,optionally including one or more or each of the first through fifthexamples, the method further comprises: performing a diagnostic scan ofthe imaging subject according to the graphical prescription, includingperforming one or more saturation pulses at a location dictated by thesaturation band.

The disclosure also provides support for a method, comprising: acquiringa medical image, labeling the medical image with ground truthparameters, mapping predicted plane parameters to the medical imageusing a deep neural network, comparing the predicted plane parameterswith the ground truth parameters and computing loss based on adifference between the predicted plane parameters and the ground truthparameters, and adjusting weights and biases of the deep neural networkbased on loss to train the deep neural network to output a planeprojection based on a medical image input into the deep neural network.In a first example of the method, the plane projection is a projectionof a plane where a 3D coordinate system and an image plane of themedical image intersect. In a second example of the method, optionallyincluding the first example, the ground truth parameters include a planeposition and a plane angulation of the plane projection. In a thirdexample of the method, optionally including one or both of the first andsecond examples, determining at least one of the plane position and theplane angulation includes training a regression network and implementingthe regression network to identify at least one of the plane positionand the plane angulation based on the medical image. In a fourth exampleof the method, optionally including one or more or each of the firstthrough third examples, determining at least one of the plane positionand the plane angulation includes: acquiring the medical image,generating a saturation band based on the medical image, and determiningthe plane position and the plane angulation based on a band position anda band angulation of the saturation band, respectively. In a fifthexample of the method, optionally including one or more or each of thefirst through fourth examples, generating the saturation band includesgenerating a segmentation mask of the medical image to identify ananatomy of interest of the medical image. In a sixth example of themethod, optionally including one or more or each of the first throughfifth examples, the anatomy of interest includes at least one curvature.In a seventh example of the method, optionally including one or more oreach of the first through sixth examples, generating the saturation bandfor the anatomy of interest includes: identifying a first curvature ofthe anatomy of interest using a first curvature-based threshold, fittinga first plane to anterior points of the first curvature, and positioningan anterior saturation band parallel to and offset from the first planeby a pre-determined distance. In an eighth example of the method,optionally including one or more or each of the first through seventhexamples, generating the saturation band for the anatomy of interestfurther includes: identifying a second curvature of the anatomy ofinterest using a second curvature-based threshold, fitting a secondplane to inferior points of the second curvature, and positioning aninferior saturation band parallel to and offset from the second plane bya pre-determined distance. In a ninth example of the method, optionallyincluding one or more or each of the first through eighth examples,generating the saturation band comprises: mapping at least one boundingbox to the anatomy of interest, identifying the plane having a normalclosest to a direction of the segmentation mask, using left, posterior,superior (LPS) cosine directions from the segmentation mask to identifya first direction of a bounding box which has a highest similarity witha second direction, opposite the first direction, adjusting a centerpoint of the bounding box distal from the normal in either firstdirection or the second direction, and identifying plane parameters ofthe plane. In a tenth example of the method, optionally including one ormore or each of the first through ninth examples, each of the at leastone bounding boxes are mapped to anatomical landmarks of the anatomy ofinterest. In an eleventh example of the method, optionally including oneor more or each of the first through tenth examples, the deep neuralnetwork comprises a plurality of convolutional filters, wherein asensitivity of each of the plurality of convolutional filters ismodulated by a corresponding spatial regularization factor.

The disclosure also provides support for an imaging system comprising:an imaging device, a memory, storing: a trained convolutional neuralnetwork (CNN), and instructions, a display device, and a processorcommunicably coupled to the imaging device, the display device, and thememory, and when executing the instructions, configured to: acquire amedical image of an imaging subject via the imaging device, map a planemask for the imaging subject using the trained CNN, generate asaturation band based on the plane mask, and display a graphicalprescription on the display device, the graphical prescription includingthe saturation band overlaid on the medical image.

When introducing elements of various embodiments of the presentdisclosure, the articles “a,” “an,” and “the” are intended to mean thatthere are one or more of the elements. The terms “first,” “second,” andthe like, do not denote any order, quantity, or importance, but ratherare used to distinguish one element from another. The terms“comprising,” “including,” and “having” are intended to be inclusive andmean that there may be additional elements other than the listedelements. As the terms “connected to,” “coupled to,” etc. are usedherein, one object (e.g., a material, element, structure, member, etc.)can be connected to or coupled to another object regardless of whetherthe one object is directly connected or coupled to the other object orwhether there are one or more intervening objects between the one objectand the other object. In addition, it should be understood thatreferences to “one embodiment” or “an embodiment” of the presentdisclosure are not intended to be interpreted as excluding the existenceof additional embodiments that also incorporate the recited features.

In addition to any previously indicated modification, numerous othervariations and alternative arrangements may be devised by those skilledin the art without departing from the spirit and scope of thisdescription, and appended claims are intended to cover suchmodifications and arrangements. Thus, while the information has beendescribed above with particularity and detail in connection with what ispresently deemed to be the most practical and preferred aspects, it willbe apparent to those of ordinary skill in the art that numerousmodifications, including, but not limited to, form, function, manner ofoperation and use may be made without departing from the principles andconcepts set forth herein. Also, as used herein, the examples andembodiments, in all respects, are meant to be illustrative only andshould not be construed to be limiting in any manner.

1. A method, comprising: acquiring a localizer image of an imagingsubject; determining a plane mask for the localizer image by enteringthe localizer image as input to a deep neural network trained to outputthe plane mask based on the localizer image; generating a saturationband based on the plane mask by positioning the saturation band at aposition and an angulation of the plane mask; and outputting a graphicalprescription for display on a display device, the graphical prescriptionincluding the saturation band overlaid on the localizer image.
 2. Themethod of claim 1, wherein the plane mask is a projection of a plane ofa 3D coordinate system onto the localizer image.
 3. The method of claim1, wherein overlaying the saturation band on the localizer imageincludes positioning the saturation band at the position and theangulation of the plane mask.
 4. The method of claim 3, wherein at leastone of a position and an angulation of the saturation band aredetermined based on plane fitting plane parameters of the plane mask. 5.The method of claim 1, further comprising determining a width of thesaturation band in response to input received from a user input device.6. The method of claim 1, further comprising adjusting the graphicalprescription, including at least one of a position, an angulation, and awidth of the saturation band, based on user input.
 7. The method ofclaim 1, further comprising performing a diagnostic scan of the imagingsubject according to the graphical prescription, including performingone or more saturation pulses at a location dictated by the saturationband.
 8. A method, comprising: acquiring a medical image; labeling themedical image with ground truth parameters; mapping predicted planeparameters to the medical image using a deep neural network; comparingthe predicted plane parameters with the ground truth parameters andcomputing loss based on a difference between the predicted planeparameters and the ground truth parameters; and adjusting weights andbiases of the deep neural network based on loss to train the deep neuralnetwork to output a plane projection based on a medical image input intothe deep neural network.
 9. The method of claim 8, wherein the planeprojection is a projection of a plane where a 3D coordinate system andan image plane of the medical image intersect.
 10. The method of claim8, wherein the ground truth parameters include a plane position and aplane angulation of the plane projection.
 11. The method of claim 10,wherein determining at least one of the plane position and the planeangulation includes training a regression network and implementing theregression network to identify at least one of the plane position andthe plane angulation based on the medical image.
 12. The method of claim10, wherein determining at least one of the plane position and the planeangulation includes: acquiring the medical image; generating asaturation band based on the medical image; and determining the planeposition and the plane angulation based on a band position and a bandangulation of the saturation band, respectively.
 13. The method of claim12, wherein generating the saturation band includes generating asegmentation mask of the medical image to identify an anatomy ofinterest of the medical image.
 14. The method of claim 13, wherein theanatomy of interest includes at least one curvature.
 15. The method ofclaim 13, wherein generating the saturation band for the anatomy ofinterest includes: identifying a first curvature of the anatomy ofinterest using a first curvature-based threshold; fitting a first planeto anterior points of the first curvature; and positioning an anteriorsaturation band parallel to and offset from the first plane by apre-determined distance.
 16. The method of claim 13, wherein generatingthe saturation band for the anatomy of interest further includes:identifying a second curvature of the anatomy of interest using a secondcurvature-based threshold; fitting a second plane to inferior points ofthe second curvature; and positioning an inferior saturation bandparallel to and offset from the second plane by a pre-determineddistance.
 17. The method of claim 13, wherein generating the saturationband comprises: mapping at least one bounding box to the anatomy ofinterest; identifying the plane having a normal closest to a directionof the segmentation mask; using left, posterior, superior (LPS) cosinedirections from the segmentation mask to identify a first direction of abounding box which has a highest similarity with a second direction,opposite the first direction; adjusting a center point of the boundingbox distal from the normal in either first direction or the seconddirection; and identifying plane parameters of the plane.
 18. The methodof claim 17, wherein each of the at least one bounding boxes are mappedto anatomical landmarks of the anatomy of interest.
 19. The method ofclaim 8, wherein the deep neural network comprises a plurality ofconvolutional filters, wherein a sensitivity of each of the plurality ofconvolutional filters is modulated by a corresponding spatialregularization factor.
 20. An imaging system comprising: an imagingdevice; a memory, storing: a trained convolutional neural network (CNN);and instructions; a display device; and a processor communicably coupledto the imaging device, the display device, and the memory, and whenexecuting the instructions, configured to: acquire a medical image of animaging subject via the imaging device; map a plane mask for the imagingsubject using the trained CNN; generate a saturation band based on theplane mask; and display a graphical prescription on the display device,the graphical prescription including the saturation band overlaid on themedical image.